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PRODID:-//Institute of Statistical Research and Training - ECPv4.7.4//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Institute of Statistical Research and Training
X-ORIGINAL-URL:https://www.isrt.ac.bd
X-WR-CALDESC:Events for Institute of Statistical Research and Training
BEGIN:VEVENT
DTSTART;TZID=UTC+6:20230910T170000
DTEND;TZID=UTC+6:20231003T170000
DTSTAMP:20230930T140442
CREATED:20230811T065741Z
LAST-MODIFIED:20230818T183544Z
UID:5968-1694365200-1696352400@www.isrt.ac.bd
SUMMARY:Training programs on Applied Statistics for Data Science using SPSS and Stata to begin from September 10\, 2023
DESCRIPTION:The upcoming SPSS and Stata training programs will begin on Septermber 10\, 2023. For further details (program schedule\, registration etc.) please click on the link https://www.isrt.ac.bd/training/spss-and-stata/ \n
URL:https://www.isrt.ac.bd/event/spss-and-stata-training-programs-to-begin-from-september-10-2023/
LOCATION:Bangladesh
CATEGORIES:training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC+6:20230918T140000
DTEND;TZID=UTC+6:20230918T153000
DTSTAMP:20230930T140442
CREATED:20230914T064104Z
LAST-MODIFIED:20230914T064619Z
UID:6029-1695045600-1695051000@www.isrt.ac.bd
SUMMARY:Applied Statistics and Data Science Seminar on Monday\, September 18\, 2023
DESCRIPTION:Title: The Generalized Variable Importance Metric: A model agnostic method to identify predictor outcome relationship \nPresenter: \nKaviul Anam khan \nPhD in Biostatistics candidate at the Dalla Lana School of Public Health\, University of Toronto \nAssistant Professor\, Department of Statistical Sciences\, University of Toronto \n \nAbstract: \nThe aim my research is to define importance of predictors for black box machine learning methods\, where the prediction function can be highly non-additive and cannot be represented by statistical parameters. In this paper we defined a “Generalized Variable Importance Metric (GVIM)” using the true conditional expectation function for a continuous or a binary response variable. We further showed that the defined GVIM can be represented as a function of the Conditional Average Treatment Effect (CATE) squared for multinomial and continuous predictors. Then we propose how the metric can be estimated using any machine learning models. Finally we showed the properties of the estimator using multiple simulations. While the estimators for the GVIM are consistent\, they have small sample biases. We proposed and efficient influence function based approach under some regularity conditions to perform one step correction of the bias. This research is going to significantly impact the public and clinical health sciences\, since this opens the door for effectively using modern machine learning methods in real life applications in health sciences. \n\n
URL:https://www.isrt.ac.bd/event/6029/
LOCATION:Bangladesh
CATEGORIES:seminar
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