IASS Webinar 22: Hukum Chandra Prize 2022: Small area estimation: a novel approach on estimation of mean squared prediction error of small-area predictors

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Date(s) - 26/10/2022
2:00 pm - 3:30 pm

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IASS Webinar 22: Hukum Chandra Prize 2022: Small area estimation: a novel approach on estimation of mean squared prediction error of small-area predictors

26 October 2022 at 2pm – 3:30pm (CET)


All are invited to the webinar, organised by the International Association for Survey Statisticians.


Please register for the IASS Webinar at:



After registering, you will receive a confirmation email containing information about joining the webinar. There will be time for questions. The webinar will be recorded and made available on the IASS and ISI web site. See below for the abstract and biography of the speakers.


The Hukum Chandra prize is awarded to a mid-career researcher, defined as someone with more than 10 years of experience after PhD or in employment, who has made an important contribution in research areas of Hukum Chandra’s work namely, survey sampling, small area estimation, official statistics, spatial analysis applied to official and survey statistics and agricultural statistics. The winner of the 2022 Hukum Chandra Prize is: Mahmoud Torabi


Webinar Abstract

In recent years there has been substantial interest in small area estimation (SAE) that is largely driven by practical demands. In policy making regarding the allocation of resources to subgroups (small areas), or determination of subgroups with specific characteristics (e.g. in health and medical studies) in a population, it is desirable that the decisions are made on the basis of reliable estimates.  A major topic in SAE is estimation of mean-squared prediction errors (MSPEs) for predictors of various characteristics of interest that are associated with the small areas. We propose a simple, unified, Monte-Carlo assisted approach to second-order unbiased estimation of MSPE of a small area predictor. The proposed MSPE estimator is easy to derive, has a simple expression, and applies to a broad range of predictors that include the traditional empirical best linear unbiased predictor (EBLUP), empirical best predictor (EBP), and post model selection EBLUP and EBP as special cases. Theoretical and empirical results demonstrate properties and advantages of the proposed MSPE estimator.

Speaker:  Mahmoud Torabi



Mahmoud Torabi is a Professor of Biostatistics in the Department of Community Health Sciences at the University of Manitoba, Canada. He is also an adjunct professor in the Department of Statistics and a scientist in the Children’s Hospital Research Institute of Manitoba (CHRIM) at the University of Manitoba. His main research areas are spatial statistics and small area estimation. He has received some provincial and national funding for his research. He has published more than 70 papers in peer-reviewed statistics and health research journals and served as a referee for over 100 papers. He is a member of editorial board of some statistics journals such as Electronic Journal of Statistics, Environmetrics, and Survey Methodology. He has served the Statistical Society of Canada in various capacities including President of the Survey Methods Section.