Abstract Number:
3689
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Abhishek Mandal (1), Debajyoti Sinha (2), Dipankar Bandyopadhyay (3), Antonio Linero (1)
Institutions:
(1) N/A, N/A, (2) Florida State University, N/A, (3) Virginia Commonwealth University, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Complex surveys have garnered substantial significance across diverse domains, spanning social sciences, public health, and market research. Their pivotal role lies in furnishing representative estimations while adeptly addressing the intricacies of survey design effects. When faced with the intricate complexities arising from the unknown effects of various covariates, parametric approaches may prove insufficient in handling the nuances associated with survey design impacts. Additionally, the Gaussian error distributional assumption would be inappropriate in many applications where the response distribution is heavy-tailed or skewed. This paper introduces the Bayesian Additive Regression Trees (BART) framework-a potent and adaptable approach tailored for analysing intricate survey data, specifically with subject weights. We propose an extension of BART to model heavy-tailed and skewed error distribution while considering subject weights. Its ability to account for the survey design features, handle non-linearity, and provide uncertainty estimates makes it a valuable tool for researchers and practitioners working with complex survey data.
Keywords:
Bayesian nonparametrics|Bayesian additive regression trees|Complex survey| | |
Sponsors:
Section on Bayesian Statistical Science
Tracks:
Bayesian nonparametrics
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