Social Statistics Seminar - 2 November 2021
|Dates:||2 November 2021|
|Times:||16:00 - 17:00|
|What is it:||Seminar|
|Organiser:||School of Social Sciences|
|Who is it for:||University staff, External researchers, Adults, Alumni, Current University students|
Social Statistics Seminars 2020/21 – November Session
A general multiply robust framework for combining probability and non-probability samples in surveys
David Haziza (University of Ottawa)
Join us at 4pm (GMT) on 2 November 2021!
Registration link: http://bit.ly/socialstats1121
Please register using your full name and your email address.
In recent years, there has been an increased interest in combining probability and nonprobability samples. Non-probability sample are cheaper and quicker to conduct but the resulting estimators are vulnerable to bias as the participation probabilities are unknown. To adjust for the potential bias, estimation procedures based on parametric or nonparametric models have been suggested in the literature. However, the validity of the resulting estimators relies heavily on the validity of the underlying models. We propose a data integration approach by combining multiple outcome regression models and participation models. The proposed approach can be used for estimating general parameters including totals, means, distribution functions and percentiles. The resulting estimators are multiply robust in the sense that they remain consistent if all but one model are misspecified. I will present the results from a simulation study that show the benefits of the proposed method in terms of bias and efficiency.
About the speaker
David Haziza is a professor at the Department of Mathematics and Statistics at the University of Ottawa (Canada). His research focuses on advancing the area of survey sampling and inference. In his recent work , he explores the use of machine-learning techniques for survey estimates in the presence of missing data and influential units.
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