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X-WR-CALNAME:Institute of Applied Statistics and Data Science
X-ORIGINAL-URL:https://isrt.ac.bd
X-WR-CALDESC:Events for Institute of Applied Statistics and Data Science
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TZID:UTC
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DTSTART:20070101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20120218T150000
DTEND;TZID=UTC:20120218T160000
DTSTAMP:20170813T205618Z
CREATED:20170813T205618Z
LAST-MODIFIED:20170813T205618Z
UID:1511-1329577200-1329580800@isrt.ac.bd
SUMMARY:Seminar on Saturday\, February 18\, 2012
DESCRIPTION:An overview of clinical research: Observational studies\n\n\nFebruary 14\, 2012 – 10:15am \n\n\n\nFull Title:\nAn overview of clinical research: Observational studies\n\n\nSpeaker:\nShomoita Alam\n\n\n\nCentre for Communicable Diseases\, icddr\,b\n\n\nDate/Time:\nSaturday\, February 18\, 2012\, 3:00pm\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nIn clinical research\, not only understanding the association between disease and exposure is important\, but also to know whether the exposure caused the disease holds equal concern. If it can be showed that the exposure caused the outcome\, the impact of the outcome can be evaluated by intervening on the exposure. An ideal experiment is unrealistic\, given that the same individual is followed for outcome under identical conditions with and without the exposure\, by going back in time. To mimic such counterfactual\, one possible way is to design a randomized control trial (RCT) where the exposure is assigned by the investigator and followed up till the outcome occurs. In many cases it may not be a feasible option to randomly assign exposure of interest in RCT due to ethical issues or as it is cost prohibitive. The best alternative is conducting observational studies. Observational studies can be either analytical or descriptive. Analytical studies feature a comparison (control) group\, whereas descriptive studies do not. Hypotheses about causation from descriptive studies are often tested in rigorous analytical studies. Within analytical studies\, cohort studies\, case control studies and cross sectional studies are popularly used among epidemiologists based on their research objective.
URL:https://isrt.ac.bd/event/seminar-on-saturday-february-18-2012/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20120129T150000
DTEND;TZID=UTC:20120129T160000
DTSTAMP:20170813T205807Z
CREATED:20170813T205807Z
LAST-MODIFIED:20170813T205807Z
UID:1513-1327849200-1327852800@isrt.ac.bd
SUMMARY:Seminar on Sunday\, January 29\, 2012
DESCRIPTION:Statistical method for the analysis of functional connections of multiple spike trains\n\n\nJanuary 19\, 2012 – 4:43pm \n\n\n\nFull Title:\nStatistical method for the analysis of functional connections of multiple spike trains\n\n\nSpeaker:\nMohammad Shahed Masud\, PhD\n\n\n\nInstitute of Statistical Research and Training\, University of Dhaka\n\n\nDate/Time:\nSunday\, January 29\, 2012\, 3:00pm\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nDevelopment of Multi-Electrode Arrays (MEA) enables researchers to record multiple spike trains simultaneously from associated neurons. Simultaneously recorded multiple spike trains are used to study how groups of neurons process information and how they interact with each other. Developing a statistical method for analyzing multiple spike trains and\, in particular\, estimating the functional connection between spike trains is a challenging problem that has resulted in substantial research. In this study a statistical method\, the Cox method is presented for analyzing functional connection of simultaneously recorded multiple spike trains. This method estimates a vector of influence strengths from multiple spike trains (called reference trains) to the selected (target) spike train. Selecting another target spike train and repeating the calculation of the influence strengths from the reference spike trains enables researchers to find all functional connections among multiple spike trains. In order to study functional connection an ‘influence function’ is identified. This function recognizes the specificity of neuronal interactions and reflects the dynamics of postsynaptic potential. The Cox method has been thoroughly tested using multiple sets of data generated by the neural network model of the leaky integrate and fire neurons with a prescribed architecture of connections. The results suggest that this method is highly successful for analyzing functional connection of simultaneously recorded multiple spike trains.
URL:https://isrt.ac.bd/event/seminar-on-sunday-january-29-2012/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20120118T150000
DTEND;TZID=UTC:20120118T160000
DTSTAMP:20170814T014454Z
CREATED:20170814T014454Z
LAST-MODIFIED:20170814T014454Z
UID:1515-1326898800-1326902400@isrt.ac.bd
SUMMARY:Seminar on Wednesday\, January 18\, 2012
DESCRIPTION:Functional data analysis and its applications\n\n\n\nJanuary 9\, 2012 – 9:41am \n\n\n\nFull Title:\nFunctional data analysis and its application\n\n\nSpeaker:\nM. Shahid Ullah\, PhD\n\n\n\nFlinders University\, Adelaide\, Australia\n\n\nDate/Time:\nWednesday\, January 18\, 2012\, 3:00pm\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nThe Functional Data Analysis (FDA) approach is proposed towards modelling time series data because of recognition of the need to better analyse\, model and forecast data observed over time. This approach allows for smooth functions of age\, is robust for outlying years due to wars and epidemics\, and provides a modelling framework that is easily adapted to allow for constraints and other information. Ideas from functional data analysis\, nonparametric smoothing and robust statistics are combined to form a methodology that is widely applicable to any functional time series data observed discretely and possibly with error. The model is a generalization of the Lee–Carter (LC) model and is applied to French mortality data\, Australian fertility data and Finish injury data\, and the forecasts obtained are shown to be superior to those from the LC method and several of its variants. A new approach called Forecast REsidual Sum of Squares (FRESS) is also proposed to check the forecast accuracy. Using the specific example of Finish injury data\, FRESS demonstrated that FDA is superior over the more commonly reported ordinary least square\, Poisson and negative binomial modelling approaches in terms of forecast accuracy.
URL:https://isrt.ac.bd/event/seminar-on-wednesday-january-18-2012/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20111228T120000
DTEND;TZID=UTC:20111228T130000
DTSTAMP:20170814T014705Z
CREATED:20170814T014705Z
LAST-MODIFIED:20170814T014705Z
UID:1517-1325073600-1325077200@isrt.ac.bd
SUMMARY:Seminar on Wednesday\, December 28\, 2011
DESCRIPTION:Bayesian Penalized Methods for High-Dimensional Data\n\n\nDecember 16\, 2011 – 7:37am \n\n\n\nFull Title:\nBayesian Penalized Methods for High-Dimensional Data and Network Analysis\n\n\nSpeaker:\nZakaria S Khondker\n\n\n\nUniversity of North Carolina at Chapel Hill\, USA\n\n\nDate/Time:\nWednesday\, December 28\, 2011\, 12:00 noon\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nThe curse of dimensionality boils down to dealing with too many parameters than the sample size reasonably permits. When dimension is larger than the sample size the model is unidentifiable and all the parameters are not estimable. Even when the dimension is smaller than the sample size but dimension to sample size ratio is not small enough or there is colinearity among the predictors the estimators are unstable. Penalized methods for shrinkage of parameters are becoming increasingly popular. The advent of high-dimensional data\, where the number of covariates (p) or responses (d) exceed the sample size (n)\, made traditional estimation techniques infeasible. Even for cases with sample size larger than the number of parameters shrinkage can improve performance in both mean and covariance\nparameters. \nOur first paper focuses on estimation of sparse covariance matrices and their inverse subject to positive definiteness constraints. The abundance of high-dimensional data\, where the sample size (n) is less than the dimension (d)\, requires shrinkage estimation methods since the maximum likelihood estimator is not positive definite in this case. Furthermore\, when n is larger than d but not sufficiently larger\, shrinkage estimation is more stable than maximum likelihood as it reduces the condition number of the precision matrix. Frequentist methods have utilized penalized likelihood methods\, whereas Bayesian approaches rely on matrix decompositions\, Wishart priors or graph theory for shrinkage. In this paper we propose a new Bayesian method\, called the Bayesian Covariance Lasso (BCLASSO)\, for the shrinkage estimation of a precision (covariance) matrix. We consider a class of priors for the precision matrix that leads to the popular frequentist penalties as special cases\, develop a Bayes estimator for the precision matrix\, and propose an efficient sampling scheme that does not precalculate boundaries for positive definiteness. The proposed method is permutation invariant and performs shrinkage and estimation simultaneously for non-full rank data. Simulations show that the proposed BCLASSO performs similarly as frequentist methods for non-full rank data. \nOur second paper focuses on estimation of the matrix of regression coefficients for high-dimensional multivariate response. The common approaches for dimension reduction in high-dimensional data include variable selection and penalized regression. Penalized approaches like lasso\, adaptive lasso\, SCAD\, and Bayesian lasso have been used for the estimation of mean parameters for multivariate response. A less explored approach for multivariate response involves dimension reduction via reduced rank decomposition of the regression coefficient matrix to take advantage of correlations among the regression coefficients that arises due to correlation among both responses and predictors. The approach may be advantageous when genes work in unison affecting each other and small effects of many genes may add up to a larger phonotypical impact. We first derive the framework for L1 priors on multivariate coefficient matrix in traditional approach. Then we develop the generalized low rank regression (GLRR) model under L2 priors and derive the framework for L1 priors. Simulations and application to ADNI data suggest that GLRR has great advantage over traditional approaches. It greatly reduces the number of parameters while performing much better; comparative performance gets even better for higher dimensions.
URL:https://isrt.ac.bd/event/seminar-on-wednesday-december-28-2011/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20111126T143000
DTEND;TZID=UTC:20111126T153000
DTSTAMP:20170814T014913Z
CREATED:20170814T014913Z
LAST-MODIFIED:20170814T014913Z
UID:1519-1322317800-1322321400@isrt.ac.bd
SUMMARY:Seminar on Saturday\, November 26\, 2011
DESCRIPTION:Multi-stratum and split-plot designs in Industrial experiments\n\n\nNovember 19\, 2011 – 7:49pm \n\n\n\nFull Title:\nMulti-stratum and split-plot designs in Industrial experiments\n\n\nSpeaker:\nM. Lutfor Rahman\n\n\n\nSchool of Mathematical Sciences\, Queen Mary\, University of London\n\n\nDate/Time:\nSaturday\, November 26\, 2011\, 2:30 PM\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nHard-to-set factors lead to split-plot type designs and mixed models. Mixed models are used to analyze multi-stratum designs as each stratum may have random effects on the responses. It is usual to use residual maximum likelihood (REML) to estimate random effects and generalized least squares (GLS) to estimate fixed effects. But a typical property of REML-GLS estimation is that it gives highly undesirable and misleading conclusions in non-orthogonal split-plot designs with few main plots. To overcome the problem a Bayesian method considering informative priors for variance components and using Markov chain Monte Carlo (MCMC) sampling would be an alternative approach. In the current study we have implemented MCMC techniques in two industrial experiments. Mixed binary logit and mixed cumulative logit models were considered for binary and categorical responses respectively. Deviance information criterion (DIC) was used to choose the best models in different scenarios.
URL:https://isrt.ac.bd/event/seminar-on-saturday-november-26-2011/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20110209T150000
DTEND;TZID=UTC:20110209T160000
DTSTAMP:20170814T015102Z
CREATED:20170814T015102Z
LAST-MODIFIED:20170814T015102Z
UID:1521-1297263600-1297267200@isrt.ac.bd
SUMMARY:Seminar on Wednesday\, February 9\, 2011
DESCRIPTION:Robustness of testing correlation and equality of variances\n\n\nFebruary 9\, 2011 – 4:41am \n\n\n\nFull Title:\nRobustness of testing correlation and equality of variances\n\n\nSpeaker:\nAnwar H Joarder\, PhD\n\n\n\nKing Fahd University of Petroleum & Minerals\nDhahran\, Saudi Arabia\n\n\nDate/Time:\nWednesday\, February 9\, 2011\, 1500\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nMany statistical methods say testing correlation and equality of variances\, have been developed under the assumptions of independence\, identicality and normality of observations. In this talk\, we will address how far we can relax the three strong assumptions with special reference to distributions that shares many intrinsic properties of normal distribution but distinct in many respects. These are bivariate t-distribution\, bivariate contaminated normal distribution and many other distributions belonging to bivariate elliptical distributions.
URL:https://isrt.ac.bd/event/seminar-on-wednesday-february-9-2011/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20110205T150000
DTEND;TZID=UTC:20110205T160000
DTSTAMP:20170814T015235Z
CREATED:20170814T015235Z
LAST-MODIFIED:20170814T015235Z
UID:1523-1296918000-1296921600@isrt.ac.bd
SUMMARY:Seminar on Saturday\, February 5\, 2011
DESCRIPTION:Risk Prediction Models for Assessing 30-Day & Long Term Mortality\n\n\nFebruary 9\, 2011 – 4:39am \n\n\n\nFull Title:\nRisk Prediction Models for Assessing 30-Day & Long Term Mortality Following CABG/AVR in Australian Cohort\n\n\nSpeaker:\nBaki Billah\, PhD\n\n\n\nDepartment of Epidemiology and Preventive Medicine\nMonash University\, Australia\n\n\nDate/Time:\nSaturday\, February 5\, 2011\, 1500\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nNot available
URL:https://isrt.ac.bd/event/seminar-on-saturday-february-5-2011/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20110118T150000
DTEND;TZID=UTC:20110118T160000
DTSTAMP:20170814T015411Z
CREATED:20170814T015411Z
LAST-MODIFIED:20170814T015411Z
UID:1525-1295362800-1295366400@isrt.ac.bd
SUMMARY:Seminar on Tuesday\, January 18\, 2011
DESCRIPTION:Visual statistical inference for regression parameters\n\n\nJanuary 15\, 2011 – 6:28am \n\n\n\nFull Title:\nVisual statistical inference for regression parameters\n\n\nSpeaker:\nMahbubul Majumder\, MSc\n\n\n\nDepartment of Statistics\, Iowa State University\, USA\n\n\nDate/Time:\nTuesday\, January 18\, 2011\, 3:00 PM\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nStatistical graphics play a crucial role in exploratory data analysis\, model checking and diagnosis. Until recently there were no formal visual methods in place for determining statistical significance of findings. This changed\, when Buja et al. (2009) conceptually introduced two protocols for formal tests of visual findings. In this paper we take this a step further by comparing the lineup protocol (Buja et al. 2009) against classical statistical testing of the significance of regression model parameters. A human subjects experiment is conducted using simulated data to provide controlled conditions. Results suggest that the lineup protocol provides results equivalent to the uniformly most powerful (UMP) test and for some scenarios yields better power than the UMP test.
URL:https://isrt.ac.bd/event/seminar-on-tuesday-january-18-2011/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20101223T123000
DTEND;TZID=UTC:20101223T133000
DTSTAMP:20170814T020253Z
CREATED:20170814T020253Z
LAST-MODIFIED:20170814T020253Z
UID:1533-1293107400-1293111000@isrt.ac.bd
SUMMARY:Seminar on Thursday\, December 23\, 2010
DESCRIPTION:Application of propensity scores in epidemiology\n\n\nDecember 21\, 2010 – 11:21pm \n\n\n\nFull Title:\nApplication of propensity scores in epidemiology\n\n\nSpeaker:\nEhsan Karim\, MSc\n\n\n\nDepartment of Statistics\nUniversity of British Columbia\nVancouver\, BC\, Canada\n\n\nDate/Time:\nThursday\, December 23\, 2010\, 1215\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nRandomization is recognized as a tool for removing bias in any experiment. In particular\, in Epidemiologic treatment group comparisons\, this tool is considered as a gold standard. However\, in a practical scenario\, at times\, the experiments may be poorly designed\, or may not be executed properly\, and therefore\, bias may creep in. Also\, having access to huge amount of non-randomized observational hostital data forces us to develop statistical framework of utilizing non-randomized data. Various approaches had been suggested in the literature to ensure comparability of such treated and untreated groups. The approach of propensity scores is one way to adjust for these bias sources in absense of randomization. In this talk\, I will discuss briefly the main concepts of how propensity scores are utilized\, implemented (in theory and in R/Stata/SAS) and interpreted in these type of situations\, with special emphasis on Epidemiologic data.
URL:https://isrt.ac.bd/event/seminar-on-thursday-december-23-2010-4/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20101223T113000
DTEND;TZID=UTC:20101223T130000
DTSTAMP:20170814T020055Z
CREATED:20170814T020055Z
LAST-MODIFIED:20170814T020055Z
UID:1531-1293103800-1293109200@isrt.ac.bd
SUMMARY:Seminar on Thursday\, December 23\, 2010
DESCRIPTION:Variable selection technique for AFT models\n\n\nDecember 21\, 2010 – 11:19pm \n\n\n\nFull Title:\nVariable selection technique for AFT models with regularized weighted least squares when p > n\n\n\nSpeaker:\nMd Hasinur Rahaman Khan\, MSc\n\n\n\nDepartment of Statistics\nUniversity of Warwick\nCoventry\, UK\n\n\nDate/Time:\nThursday\, December 23\, 2010\, 1130\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nVariable selection is fundamental to high-dimensional survival modeling. The regularized least squares method with appropriate penalty is a widely used method for simultaneous variable selection and coefficient estimation in linear regression\, including accelerated failure time (AFT) model in survival analysis. I will discuss a variable selection technique which is based on a regularized weighted least squares method. Quadratic programming is used to solve this regularized weighted least squares objective function with  constraints and constraints that come due to the censored observations from the right censored data. This technique can be applied to survival data with  e.g. microarray data set. Simulation studies and real data example will be provided for illustration. In the beginning of this talk I will discuss the censoring effect on the Kaplan-Meier estimates when\ndata are generated from different skewed failure time distributions.
URL:https://isrt.ac.bd/event/seminar-on-thursday-december-23-2010-3/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20101223T100000
DTEND;TZID=UTC:20101223T233000
DTSTAMP:20170814T015858Z
CREATED:20170814T015858Z
LAST-MODIFIED:20170814T015858Z
UID:1529-1293098400-1293147000@isrt.ac.bd
SUMMARY:Seminar on Thursday\, December 23\, 2010
DESCRIPTION:Simultaneous confidence intervals for multinomial proportions\n\n\nDecember 21\, 2010 – 11:16pm \n\n\n\nFull Title:\nSimultaneous confidence intervals for multinomial proportions\, their differences and ratios\n\n\nSpeaker:\nAzaz Bin Sharif\, MSc\n\n\n\nDepartment of Epidemiology & Biostatistics\nUniversity of Western Ontario\nLondon\, ON\, Canada\n\n\nDate/Time:\nThursday\, December 23\, 2010\, 1015\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nEstimation of simultaneous confidence intervals for multinomial proportions\, their differences and ratios is a long-standing problem in the literature. Existing methods suffer either from enforced symmetry and/or conservative coverage. We propose calculating confidence limits using the Wilson method or Jeffreys procedure for the multinomial proportions and for the ratios of multinomial proportions with critical values obtained from multivariate normal distributions that accounts for correlations between contrasted hypotheses. Confidence intervals for the differences are then obtained by recovering variance estimates from limits for single proportions. Simulation study shows that proposed methods perform well for a variety of parameter combinations. Data on mutational damage in Saccharomyces cerevisiae is used to illustrate the procedures.
URL:https://isrt.ac.bd/event/seminar-on-thursday-december-23-2010-2/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20101223T093000
DTEND;TZID=UTC:20101223T103000
DTSTAMP:20170814T015710Z
CREATED:20170814T015710Z
LAST-MODIFIED:20170814T015710Z
UID:1527-1293096600-1293100200@isrt.ac.bd
SUMMARY:Seminar on Thursday\, December 23\, 2010
DESCRIPTION:Mortality modeling of white spruce and black spruce\n\n\nDecember 21\, 2010 – 11:13pm \n\n\n\nFull Title:\nMortality modeling of white spruce and black spruce using combine estimators\n\n\nSpeaker:\nSuborna Ahmed\, MSc\n\n\n\nDepartment of Forest Resources Management\nUniversity of British Columbia\nVancouver\, BC\, Canada\n\n\nDate/Time:\nThursday\, December 23\, 2010\, 0930\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nUsing combined estimator approaches regular mortality models for white spruce and black spruce across the Boreal Forest of Canada are developed. For irregular time intervals between measures a modified logistic regression was used to model the annual probability of survival where permanent sample plot (PSPs) data from Alberta used. The PSP data were divided into two regions\nto represent local scale populations\, whereas the entire dataset represented the population at the larger scale. Monte Carlo simulations were used to: i) randomly sample from each region (10% and 30% sampling intensity); ii) fit the survival model by region using the sample data; iii) use each of the\ncombined estimators to weight the regional estimated parameters and obtain a larger scale model; and iv) calculate the bias and precision for each large scale model parameter and combined estimator. Therefore\, in this research\, several combined estimators were used to combine local scale models to\nobtain a general model for the regional scale to improve model precision over naive approaches for the target species.
URL:https://isrt.ac.bd/event/seminar-on-thursday-december-23-2010/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20101208T120000
DTEND;TZID=UTC:20101208T130000
DTSTAMP:20170814T020642Z
CREATED:20170814T020642Z
LAST-MODIFIED:20170814T020642Z
UID:1535-1291809600-1291813200@isrt.ac.bd
SUMMARY:Seminar on Wednesday\, December 8\, 2010
DESCRIPTION:Public Hurricane Loss Evaluation Models: Predicting Losses of Residential Structures in the State of Florida\n\n\nDecember 21\, 2010 – 11:09pm \n\n\n\nFull Title:\nPublic Hurricane Loss Evaluation Models: Predicting Losses of Residential Structures in the State of Florida\n\n\nSpeaker:\nB. M. Golam Kibria\, PhD\n\n\n\nDepartment of Mathematics and Statistics\nFlorida International University\nMiami\, FL 33199\, USA\n\n\nDate/Time:\nWednesday\, December 8\, 2010\, 1150\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nAs an environmental phenomenon\, hurricanes cause significant property damage and loss of life in coastal areas almost every year. Although a number of commercial loss projection models have been developed to predict the property losses\, only a handful of studies are available in the public domain to predict damage for hurricane prone areas. The State of Florida has developed an open\, public model for the purpose of probabilistic assessment of risk to insured residential property associated with wind damage from hurricanes. The model comprises of the atmospheric science\, engineering and actuarial components. The atmospheric component includes modeling the track and intensity life cycle of each simulated hurricane within the Florida threat area. Based on historical hurricane statistics\, thousands of storms are\nsimulated allowing determination of the wind risk for all residential zip code locations in Florida. The wind risk information is then provided to the engineering and actuarial components to model damage and average annual loss\, respectively. The actuarial team finds the county wise loss and the total loss for the entire state of Florida. The computer center compiles all information from atmospheric science\, engineering and actuarial components\, processes all hurricane related data and completes the project. The model was submitted to the Florida Commission on Hurricane Loss Projection Methodology for approval and went through a rigorous review and was revised per the suggestions of the commission . The final model was was approved for use by the insurance companies in Florida by the commission on August 17\, 2007.\nAt every stage of the process\, statistical procedures were used to model various parameters and validate the model. This paper presents a brief summary of the main components of the model (meteorology\, vulnerability and actuarial) and then focuses on the statistical validation of the same.
URL:https://isrt.ac.bd/event/seminar-on-wednesday-december-8-2010/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20100727T150000
DTEND;TZID=UTC:20100727T160000
DTSTAMP:20170814T020807Z
CREATED:20170814T020807Z
LAST-MODIFIED:20170814T020807Z
UID:1537-1280242800-1280246400@isrt.ac.bd
SUMMARY:Seminar on Tuesday\, July 27\, 2010
DESCRIPTION:Generalized linear models with crossed nonparametric random effects\n\n\nJuly 25\, 2010 – 4:55am \n\n\n\nFull Title:\nGeneralized linear models with crossed nonparametric random effects\n\n\nSpeaker:\nM Tariqul Hasan\, PhD\n\n\n\nDepartment of Mathematics and Statistics\nUniversity of New Brunswick\nFredericton\, NB\, Canada\n\n\nDate/Time:\nTuesday\, July 27\, 2010\, 1500\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nIn social\, health and environmental sciences\, many data are collected with a known but complex underlying structure. Over the past three decades\, multilevel modeling techniques have been extensively used to account nested data structures. However the underlying data structures are often more complex and cannot be fitted into a nested structure. This complexity of the data structure may arise due to fact that the observations are cross-classified and an observation does not belong to one member of the classification. As the existing frequentist modeling approaches have limitations to analyze such data\, we propose generalized linear models with crossed nonparametric random effect. For estimating the model parameters\, we propose the estimating equation based on BLUP (best linear unbiased predictors) of random effects. We illustrate the methodology with analysis of the child respiratory illness data.
URL:https://isrt.ac.bd/event/seminar-on-tuesday-july-27-2010/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20100310T150000
DTEND;TZID=UTC:20100310T160000
DTSTAMP:20170814T020951Z
CREATED:20170814T020951Z
LAST-MODIFIED:20170814T020951Z
UID:1539-1268233200-1268236800@isrt.ac.bd
SUMMARY:Seminar on Wednesday\, March 10\, 2010
DESCRIPTION:Modified weights based generalized quasilikelihood inferences\n\n\nMarch 4\, 2010 – 12:08pm \n\n\n\nFull Title:\nModified weights based generalized quasilikelihood inferences in incomplete longitudinal binary models\n\n\nSpeaker:\nTaslim S. Mallick\, PhD\n\n\n\nDepartment of Statistics\, Biostatistics & Informatics\nUniversity of Dhaka\, Bangladesh\n\n\nDate/Time:\nWednesday\, March 10\, 2010\, 3:00 pm\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nIn an incomplete longitudinal set up\, a small number of repeated responses subject to an appropriate missing mechanism along with a set of covariates are collected from a large number of independent individuals over a small period of time. In this set up\, the regression effects of the covariates are routinely estimated by solving certain inverse weights based generalized estimating equations. These inverse weights are introduced to make the estimating equation unbiased so that a consistent estimate of the regression parameter vector may be obtained. In the existing studies\, these weights are in general formulated conditional on the past responses. Since the past responses follow a correlation structure\, the present study reveals that if the longitudinal data subject to missing mechanism are generated by accommodating the longitudinal correlation structure\, the conditional weights based on past correlated responses may yield biased and hence inconsistent regression estimates. The biasness appears to get larger as the correlation increases. As a remedy\, in this study we propose a modification to the formulation of the existing weights so that weights are not affected directly or indirectly by the correlations. We then exploit these modified weights to form a weighted generalized quasilikelihood estimating equation that yields unbiased and hence consistent estimates for the regression effects irrespective of the magnitude of correlation. The efficiencies of the regression estimates follow due to the use of the true correlation structure as a separate longitudinal weight matrix in the estimating equation.
URL:https://isrt.ac.bd/event/seminar-on-wednesday-march-10-2010/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20100217T150000
DTEND;TZID=UTC:20100217T160000
DTSTAMP:20170814T021125Z
CREATED:20170814T021125Z
LAST-MODIFIED:20170814T021125Z
UID:1541-1266418800-1266422400@isrt.ac.bd
SUMMARY:Seminar on Wednesday\, February 17\, 2010
DESCRIPTION:Capacity Planning of a Perinatal Network: A Loss Network Framework\n\n\nFebruary 14\, 2010 – 3:01pm \n\n\n\nFull Title:\nCapacity Planning of a Perinatal Network: A Loss Network Framework\n\n\nSpeaker:\nMd Asaduzzaman\n\n\n\nInstitute of Statistical Research and Training\, University of Dhaka\, Bangladesh and\nUniversity of Westminster\, United Kingdom\n\n\nDate/Time:\nWednesday\, February 17\, 2010\, 3:00 pm\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nNeonatal care in the UK has been organised through ’Managed clinical networks’ (MCN’s) since 2004 so that service improves and care can be provided locally. Yet over a six month period in 2006/07\, neonatal units were shut to new admissions for an average of 24 days\, and one in ten units exceeded its capacity for intensive care for more than 50 days. The North Central London Perinatal Network (NCLPN)\, one of the 5 networks in London\, consists of six hospitals\, including UCLH. The pressure on neonatal units in the NCLPN is growing very fast\, in particular at UCLH\, where the rejection of admission of neonates from neonatal intensive care unit (NICU) and high dependency unit (HDU) is now very high due to capacity shortage. We apply a loss network model with overflow based on a continuous time Markov chain for capacity planning problem of a perinatal network\, with specific application to the NCLPN. We derive expressions for overflow and rejection probabilities for each neonatal unit of the network. Results obtained with the model are very close to those observed for the UCLH. They also show that a substantial number of intensive care cots are required to keep rejection level low. Using the model\, decisions on number of cots can be made for specific levels of admission rejection probabilities for each level of care at each neonatal unit of the network and specific levels of overflow to temporary care.
URL:https://isrt.ac.bd/event/seminar-on-wednesday-february-17-2010/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20100131T120000
DTEND;TZID=UTC:20100131T130000
DTSTAMP:20170814T021246Z
CREATED:20170814T021246Z
LAST-MODIFIED:20170814T021246Z
UID:1543-1264939200-1264942800@isrt.ac.bd
SUMMARY:Seminar on Sunday\, January 31\, 2010\,
DESCRIPTION:Bayesian adjustment methods for measurement error/misclassification in covariates\n\n\nJanuary 27\, 2010 – 6:41pm \n\n\n\nFull Title:\nBayesian Adjustment Methods for Measurement error/Misclassification in Covariates\n\n\nSpeaker:\nMd. Shahadut Hossain\, PhD\n\n\n\nDepartment of Statistics\, United Arab Emirates University\, UAE\n\n\nDate/Time:\nSunday\, January 31\, 2010\, 12:00PM\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nIn most epidemiological investigations\, the study units are people\, the outcome variable (or the response) is a health-related event\, and the explanatory variables are usually environmental and/or socio-demographic factors. The fundamental task in such investigations is to quantify the association between the explanatory variables (covariates/exposures) and the outcome variable through a suitable regression model. The accuracy of such quantification depends on how precisely the relevant covariates are measured. In many instances\, we cannot measure some of the covariates accurately. Rather\, we can measure noisy (mismeasured) versions of them. In statistical terminology\, mismeasurement in continuous covariates is known as measurement errors or errors-in-variables. Regression analyses based on mismeasured covariates lead to biased inference about the true underlying response-covariate associations. In this talk\, I will discuss a flexible parametric approach for avoiding this bias when estimating the response-covariate relationship through a logistic regression model. The performance of the proposed flexible parametric approach has been investigated through extensive simulation studies. Also\, the performance of the proposed method has been demonstrated on a real-life data set. Though emphasis is put on the logistic regression model\, the proposed method is unified and is applicable to the other generalized linear models\, and to other types of non-linear regression models as well.
URL:https://isrt.ac.bd/event/seminar-on-sunday-january-31-2010/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20100116T120000
DTEND;TZID=UTC:20100116T130000
DTSTAMP:20170814T021425Z
CREATED:20170814T021425Z
LAST-MODIFIED:20170814T021425Z
UID:1545-1263643200-1263646800@isrt.ac.bd
SUMMARY:Seminar on Saturday\, January 16\, 2010
DESCRIPTION:Bayesian variable selection for parametric AFT models\n\n\nJanuary 12\, 2010 – 1:07pm \n\n\n\nFull Title:\nBayesian variable selection for parametric accelerated failure time models in high dimensions\n\n\nSpeaker:\nMd Hasinur Rahaman Khan\, MSc\n\n\n\nDepartment of Statistics\, University of Warwick\, United Kingdom and\nInstitute of Statistical Research and Training\, University of Dhaka\, Bangladesh\n\n\nDate/Time:\nSaturday\, January 16\, 2010\, 12:00PM\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nNowadays high-throughput technologies are generating many types of high-dimensional data such as genomic and proteomic data and meta-data in survival analysis. One of the needs of analysis with such failure time data with very high-dimensional covariates is to obtain a system-level understanding of various complex diseases. An important recent area of application is microarray data analysis\, i.e. investigating the relationship between a censored survival outcome and microarray gene expression profiles. Because of the small sample size and large number of covariates in such situations\, frequentist methods for the variable selection process can be unstable and result in over-fitting. This research focuses instead on the Bayesian approach to identifying the most influential covariates (predictors) when fitting parametric accelerated failure time (AFT) models. The performance and sensitivity of such Bayesian variable selection methods will be analysed and demonstrated using both simulated and real datasets. This study concentrates on the AFT model since it has an intuitive physical interpretation and is a useful alternative to the more popular Cox proportional hazards model.
URL:https://isrt.ac.bd/event/seminar-on-saturday-january-16-2010/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20091229T120000
DTEND;TZID=UTC:20091229T130000
DTSTAMP:20170814T021558Z
CREATED:20170814T021558Z
LAST-MODIFIED:20170814T021558Z
UID:1547-1262088000-1262091600@isrt.ac.bd
SUMMARY:Seminar on Tuesday\, December 29\, 2009
DESCRIPTION:Statistical models for studies of health effects of air pollution\n\n\nDecember 16\, 2009 – 1:06pm \n\n\n\nFull Title:\nStatistical models for studies of health effects of air pollution\n\n\nSpeaker:\nSati Mazumdar\, PhD\n\n\n\nDepartment of Biostatistics\, University of Pittsburgh\, USA\n\n\nDate/Time:\nTuesday\, December 29\, 2009\, 12:00pm\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nGeneralized Additive Models (GAMs) with natural cubic splines (NS) as smoothing functions have become a standard analytical tool in time series studies of health effects of air pollution. However\, standard model selection procedures ignore the model uncertainty which may lead to biased estimates\, in particular those of the lagged effects. On the other hand\, the degrees of smoothing to adjust for time-varying confounders are often determined by data-driven methods such as penalized likelihood. This presentation addressed these two issues with using a Bayesian model averaging (BMA) approach to account for model uncertainty in GAMs with NS and a generalized linear mixed modeling (GLMM) approach to adjust for time-varying confounders. Firstly\, we conducted a sensitivity analysis with simulation studies for Bayesian model averaging with different calibrated hyperparameters contained in the posterior model probabilities. Our results indicated the importance of selecting the optimum degree of lagging for variables\, based not only on maximizing the likelihood\, but also by considering the possible effects of concurvity\, consistency of degree of lagging\, and biological plausibility. Simulation studies suggested that GLMM produces less biased estimates than GLM+NS. These methods were illustrated by analyses of the Allegheny County Air Pollution Study (ACAPS) where the quantity of interest was the relative risk of cardiopulmonary hospital admissions for a 20% increase in PM10 values for the current day and five previous days.
URL:https://isrt.ac.bd/event/seminar-on-tuesday-december-29-2009/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20091220T113000
DTEND;TZID=UTC:20091220T130000
DTSTAMP:20170814T021812Z
CREATED:20170814T021812Z
LAST-MODIFIED:20170814T021812Z
UID:1550-1261308600-1261314000@isrt.ac.bd
SUMMARY:Seminar on Sunday\, December 20\, 2009
DESCRIPTION:Treatment Regimes on Longitudinal Outcome Data\n\n\nDecember 17\, 2009 – 2:00pm \n\n\n\nFull Title:\nAssessing the effect of treatment regimes on longitudinal outcome data\n\n\nSpeaker:\nAbdus S. Wahed\, PhD\n\n\n\nDepartment of Biostatistics\, University of Pittsburgh\, USA\n\n\nDate/Time:\nSunday\, December 20\, 2009\, 11:30AM\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nDepression studies frequently adopt two-stage designs to examine the efficacy of augmenting pharmacotherapy with psychotherapy. Initially subjects receive one of the several treatments; if respond\, they continue the same treatment; however\, if they fail to respond\, they move to the next stage and are randomized to other treatment options. Outcome such as 24-item Hamilton Rating Scale of Depression (HRSD24) scores are then collected repeatedly to monitor the progress of the subject. The goal is to assess the effect of treatment regimes (consisting of initial treatment\, initial response and the second stage treatment combinations) on HRSD24 profile. Statistical inference for assessing treatment regimes using a summary outcome measure such as mean response has been well-studied in the literature. However\, statistical methods for assessing the effect of treatment regimes on repeated measures data are not well-developed. In this article\, we propose two methods based on mixed models and multiple imputations to assess the effect of treatment regimes on the longitudinal HRSD24 scores. Methods are compared through simulation studies and through an application to a depression dataset.
URL:https://isrt.ac.bd/event/seminar-on-sunday-december-20-2009/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20091208T120000
DTEND;TZID=UTC:20091208T130000
DTSTAMP:20170814T021936Z
CREATED:20170814T021936Z
LAST-MODIFIED:20170814T021936Z
UID:1552-1260273600-1260277200@isrt.ac.bd
SUMMARY:Seminar on Tuesday\, December 8\, 2009
DESCRIPTION:Importance of Statistics for Development\n\n\nDecember 6\, 2009 – 1:49pm \n\n\n\nFull Title:\nImportance of Statistics for Development\n\n\nSpeaker:\nShahjahan Khan\, PhD\n\n\n\nUniversity of Southern Queensland\, Australia\n\n\nDate/Time:\nTuesday\, December 8\, 2009\, 12:00PM\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nThis presentation focuses on various aspects of statistical sciences\, particularly its increasingly diverse and wide range of applications in the modern science and society. It explores the role of statistics in the evaluation of the state of affairs as well as assessing the available resources to formulate and implement collective strategies to enhance national and international development. The use of statistical methods in the decision making as well as forecasting and predicting national priorities are also covered. Furthermore\, some important contributions of statistics in health\, environment\, industrial and manufacturing sector are highlighted. Statistical measures essential to compare regional and global socio-economic development/status are discussed. Some of the remarkable impacts that statistics has made to the modern world are touched.
URL:https://isrt.ac.bd/event/seminar-on-tuesday-december-8-2009/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20091118T120000
DTEND;TZID=UTC:20091118T130000
DTSTAMP:20170814T022130Z
CREATED:20170814T022130Z
LAST-MODIFIED:20170814T022130Z
UID:1554-1258545600-1258549200@isrt.ac.bd
SUMMARY:Seminar on Wednesday\, November 18\, 2009
DESCRIPTION:Wavelet-based noise reduction of cDNA microarray images\n\n\nNovember 15\, 2009 – 1:20pm \n\n\n\nFull Title:\nWavelet-based noise reduction of cDNA microarray images\n\n\nSpeaker:\nTamanna Howlader\, PhD\n\n\n\nInstitute of Statistical Research and Training\nUniversity of Dhaka\, Bangladesh\n\n\nDate/Time:\nWednesday\, November 18\, 2009\, 12:00PM\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nRemoval of noise is an essential step in the preprocessing of microarray images for obtaining better quality gene expression measurements. Wavelet-based methods for denoising of images are very successful. However\, for cDNA microarray images\, existing methods are not efficient because they fail to take into account the signal correlation as well as the noise correlation that exists between the wavelet coefficients of the two channels. In this talk\, I consider the development of efficient wavelet-based noise reduction algorithms for cDNA microarray images that take into account these inter-channel dependencies by ‘jointly’ estimating the noise-free coefficients in both the channels. The algorithms are developed using two types of wavelet transforms\, namely\, the frequently-used discrete wavelet transform and the complex wavelet transform. Linear minimum mean squared error and maximum a posteriori estimation techniques are used to derive bivariate estimators for the noise-free coefficients of the red and green channel images by utilizing appropriate joint probability density functions for the image coefficients as well as the noise coefficients of the two channels. Extensive experimentations are carried out on a large set of cDNA microarray images to evaluate the performance of the proposed denoising methods as compared to the existing ones. Results are presented which show that the proposed methods lead to improved noise reduction performance and more accurate estimation of gene expression levels.
URL:https://isrt.ac.bd/event/seminar-on-wednesday-november-18-2009/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20090704T110000
DTEND;TZID=UTC:20090704T120000
DTSTAMP:20170814T022259Z
CREATED:20170814T022259Z
LAST-MODIFIED:20170814T022259Z
UID:1556-1246705200-1246708800@isrt.ac.bd
SUMMARY:Seminar on Saturday\, July 4\, 2009
DESCRIPTION:Interplay between Statistics and Cryptology\n\n\nJune 30\, 2009 – 1:17pm \n\n\n\nFull Title:\nInterplay between Statistics and Cryptology\n\n\nSpeaker:\nBimal K. Roy\, PhD\n\n\n\nApplied Statistics Unit\, Indian Statistical Institute (ISI)\, Kolkata\, India\n\n\nDate/Time:\nSaturday\, July 4\, 2009\, 11:00AM\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nWhen confidential or secret information are communicated through open channel\, the sender “encrypts” it with some method and the receiver “decrypts”. The encrypted information should have some “good” statistical properties so that an intruder will have “difficulty” in retrieving the actual information from the encrypted one. A particular model “stream cipher” will be introduced along with its desired statistical properties.
URL:https://isrt.ac.bd/event/seminar-on-saturday-july-4-2009/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20090523T110000
DTEND;TZID=UTC:20090523T113000
DTSTAMP:20170814T022439Z
CREATED:20170814T022439Z
LAST-MODIFIED:20170814T022439Z
UID:1558-1243076400-1243078200@isrt.ac.bd
SUMMARY:Seminar on Saturday\, May 23\, 2009
DESCRIPTION:Control of Queues by Laplace Transform\n\n\nMay 19\, 2009 – 7:22pm \n\n\n\nFull Title:\nControl of Queues by Laplace Transform\n\n\nSpeaker:\nRifat Ara Jahan\, MSc\n\n\n\nUniversity of Windsor\, Ontario\, Canada\n\n\nDate/Time:\nSaturday\, May 23\, 2009\, 11:00 AM [CANCELLED\, to be rescheduled]\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nIn this paper we apply Laplace Transform approach to control queue. We first present some relevant properties and important theorems of Laplace Transforms and Catastrophe Process. We determine the optimal time that the gatekeeper should wait after a rejected customer\, before admitting another customer by Laplace Transform approach. This optimal time depends on penalty costs and on expected interadmit times\, interaccept times and other time measurements. We verify our obtained expected times with Tang (2005) who found the expected times by setting up somewhat complex linear systems of equations. We also obtain the complete distribution of the relevant times. This Laplace Transforms approach to this problem is completely new and adds additional information to that obtained by Tang (2005). In addition\, Tang’s comments on the  system do not completely generalize her result\, whereas we give a more lengthy discussion of this case. Finally our results confirm Tang’s result and Tang’s result act as a partial verification that our results are correct. \nAbout the Speaker: \nRifat Ara Jahan did her BSc and MSc in Applied Statistics from ISRT\, University of Dhaka\, Bangladesh.
URL:https://isrt.ac.bd/event/seminar-on-saturday-may-23-2009/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20090421T120000
DTEND;TZID=UTC:20090421T130000
DTSTAMP:20170814T022616Z
CREATED:20170814T022616Z
LAST-MODIFIED:20170814T022616Z
UID:1560-1240315200-1240318800@isrt.ac.bd
SUMMARY:Seminar on Tuesday\, April 21\, 2009
DESCRIPTION:Risk of Myocardial Infarction in Patients with Psoriatic Arthritis\n\n\nApril 19\, 2009 – 2:23pm \n\n\n\nFull Title:\nRisk of Myocardial Infarction in Patients with Psoriatic Arthritis\n\n\nSpeaker:\nM. Mushfiqur Rahman\, MSc\n\n\n\nUniversity of British Columbia\, Canada \n \n\n\nDate/Time:\nTuesday\, April 21\, 2009\, 12:00PM [CANCELLED]\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nA recent study based on the General Practice Research Database in the United Kingdom found that psoriasis (Ps) is associated with an increased risk of acute myocardial infarction (AMI). Similar associations may exist between psoriatic arthritis (PsA) and AMI\, but no data are available. To address this issue\, we examined the relation between PsA and the risk of incident AMI (in patients who were admitted to the hospital for AMI) in a population – based musculoskeletal cohort database from British Columbia\, Canada. \nAbout the speaker \nM. Mushfiqur Rahman was a faculty at ISRT.
URL:https://isrt.ac.bd/event/seminar-on-tuesday-april-21-2009/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20081110T120000
DTEND;TZID=UTC:20081110T130000
DTSTAMP:20170814T022744Z
CREATED:20170814T022744Z
LAST-MODIFIED:20170814T022744Z
UID:1562-1226318400-1226322000@isrt.ac.bd
SUMMARY:Seminar on Monday\, November 10\, 2008
DESCRIPTION:Seminar : Assessing Poverty\, Risk and Vulnerability\n\n\nJanuary 24\, 2009 – 11:32pm \n\n\n\nFull Title:\nAssessing Poverty\, Risk and Vulnerability: A study on Flooded Households in Rural Bangladesh\n\n\nSpeaker:\nM. Israt Rayhan\, PhD\n\n\n\nInstitute of Statistical Research and Training\nUniversity of Dhaka\, Dhaka\nBangladesh\n\n\nDate/Time:\nMonday\, November 10\, 2008\, 12PM\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nFlood is a common catastrophe for Bangladesh. The combination of its geography\, population density\, and extreme poverty makes Bangladeshi people vulnerable to flood risks. This study is set forth to examine the poverty\, risk and vulnerability for flood hazards. A cross sectional household survey was carried out two weeks after a flood had occurred in the year 2005. In total 600 rural households were interviewed in four districts selected through a three stage stratified random sampling technique. A utilitarian approach is used to assess flood vulnerability and its components: poverty\, idiosyncratic and aggregate risks to capture the effect of flood on household’s welfare. To estimate the correlates of flood vulnerability\, a set of fixed households’ characteristics is used as explanatory variables. The results depict that poverty and idiosyncratic flood risks are positively correlated and highly significant. Households with higher educated members\, being male-headed and owner of a dwelling place have been found to be less vulnerable to idiosyncratic flood risk. Possession of arable land and a small family size can reduce poverty and the aggregate flood risk.
URL:https://isrt.ac.bd/event/seminar-on-monday-november-10-2008/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20081024T120000
DTEND;TZID=UTC:20081024T130000
DTSTAMP:20170814T023022Z
CREATED:20170814T023022Z
LAST-MODIFIED:20170814T023022Z
UID:1564-1224849600-1224853200@isrt.ac.bd
SUMMARY:Seminar on Friday\, October 24\, 2008
DESCRIPTION:Seminar : Prevention of Child Injury\nJanuary 25\, 2009 – 8:29am \n\n\n\nFull Title:\nPRECISE – Prevention of Child Injury through Social-intervention and Education\n\n\nSpeaker:\nA.K.M. Fazlur Rahman\, PhD\n\n\n\n\n\n\nDate/Time:\nFriday\, October 24\, 2008\, 12PM\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \nABSTRACT \nThirty thousand children a year die from preventable injuries\, including serious cuts and burns\, drowning\, road traffic accidents and falls. The Prevention of Child Injury through Social-intervention and Education (PRECISE) is the largest child injury intervention programme ever initiated in a developing country. The goal of the project is the reduce child and parental mortalities\, morbidities and disabilities due to injury by implementing comprehensive prevention programmes that can be reproduced in a cost effective manner.  PRECISE is currently implemented in three rural areas\, with an additional one urban control area covering a total population of 800\,000. This population is monitored using CIPRB’s unique Injury Surveillance System which records changes in injury patterns. This is a critical monitoring tool of the project.  \nThe programme’s main components include: \n\nSurvival swimming lessons for children\nCrèches to provide a safe\, nurturing environment for children (the crèches are run during the hours of the day when most accidents occur)\nA Home Safety Programme to create safe homes by removing physical hazards\nA School Safety Programme to educate school children about injury prevention
URL:https://isrt.ac.bd/event/seminar-on-friday-october-24-2008/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20080902T120000
DTEND;TZID=UTC:20080902T130000
DTSTAMP:20170814T023210Z
CREATED:20170814T023210Z
LAST-MODIFIED:20170814T023210Z
UID:1566-1220356800-1220360400@isrt.ac.bd
SUMMARY:Seminar on Tuesday\, September 2\, 2008
DESCRIPTION:Seminar : A New Class of Measures of Skewness\n\n\nSeptember 2\, 2008 – 4:22am \n\n\n\nFull Title:\nA New Class of Measures of Skewness in Univariate Population\n\n\nSpeaker:\nM. Nasser\, PhD\n\n\n\nDepartment of Statistics\, University of Rajshahi\, Rajshahi\, Bangladesh\n\n\nDate/Time:\nTuesday\, September 2\, 2008\, 1200PM\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nNot available
URL:https://isrt.ac.bd/event/seminar-on-tuesday-september-2-2008/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20080729T120000
DTEND;TZID=UTC:20080729T130000
DTSTAMP:20170814T023340Z
CREATED:20170814T023340Z
LAST-MODIFIED:20170814T023340Z
UID:1568-1217332800-1217336400@isrt.ac.bd
SUMMARY:Seminar on Tuesday\, July 29\, 2008
DESCRIPTION:Methods and Techniques of Social Research\n\n\nJuly 28\, 2008 – 4:25am \n\n\n\nFull Title:\nMethods and Techniques of Social Research\n\n\nSpeaker:\nAbu Sufian\, PhD\n\n\n\nUniversity of Bahrain\n\n\nDate/Time:\nTuesday\, July 29\, 2008\, 1200PM\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nNot available
URL:https://isrt.ac.bd/event/seminar-on-tuesday-july-29-2008/
CATEGORIES:seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20080527T120000
DTEND;TZID=UTC:20080527T130000
DTSTAMP:20170814T023518Z
CREATED:20170814T023518Z
LAST-MODIFIED:20170814T023518Z
UID:1570-1211889600-1211893200@isrt.ac.bd
SUMMARY:Seminar on Tuesday\, May 27\, 2008
DESCRIPTION:Seminar: Analysis of a Loss Network Model\n\n\nMay 26\, 2008 – 4:27am \n\n\n\nFull Title:\nAnalysis of a Loss Network Model for Performance Evaluation and Capacity Planning of a Neonatal Network in the UK\n\n\nSpeaker:\nMd. Asaduzzaman\, MSc\n\n\n\nPhD Student \nHealth and Social Care Modelling Group \nSchool of Informatics\, University of Westminster London\, UK\n\n\nDate/Time:\nTuesday\, May 27\, 2008\, 1200PM\n\n\nVenue:\nISRT Seminar Room\n\n\n\n  \n\nABSTRACT\nEvery year in the UK\, 8-13% babies are admitted to a neonatal unit and 2-3% require intensive and/or high dependency care. Neonatal care in the UK has been organised through ‘Managed clinical networks’ (MCN’s) since 2004. In England\, there are 182 neonatal units organised in 24 networks. The baby charity BLISS reported that over a six month period\, neonatal units were shut to new admissions for an average of 24 days\, and 10% units exceeded their intensive care capacity for more than 50 days. The National Audit Office reported that capacity and staffing problems at unit level continue to constrain neonatal services. The main aim of this paper is to develop a model to assist with performance measure and capacity planning of such a neonatal network. We develop a continuous-time Markov chain model with overflow to capture movement of patients into neonatal units of a network using loss network framework. We then derive expression for overflow and rejection probabilities to measure performance of the network based on decomposition approach. The model is applied to a neonatal network in North Central London. Decision on number of cot can be made for specific level of overflow and admission rejection probabilities at each neonatal unit of the network.
URL:https://isrt.ac.bd/event/seminar-on-tuesday-may-27-2008/
CATEGORIES:seminar
END:VEVENT
END:VCALENDAR