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Activity Number: 343 - Contributed Poster Presentations: Section on Bayesian Statistical Science
Type: Contributed
Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #323027
Title: A Flexible Bayesian Regression Approach for Modeling Interval Data
Author(s): Shubhajit Sen* and Kiranmoy Das
Companies: North Carolina State University and Indian Statistical Institute
Keywords: Interval data; Joint regression model; slab and spike prior; local-global shrinkage; acute lymphocytic leukemia; feature screening

We propose a new method for modeling interval data. The relationship between an interval-valued response and a set of interval-valued predictors is investigated by considering a joint regression model, one for the centers (location) of response and predictors, and the other one for the radii (imprecision). Previous works on this problem either can not obtain different regression coefficients for the center and the radii, or they do not consider the dependence between them. Our model overcomes these drawbacks as both the centers and the radii of predictors are used for predicting both the center and the radius of response with the flexibility of identifying the different effects of the center and radius of a predictor on response, along with accounting for the dependence between the center and the radius. We develop a Bayesian estimation method, with an automated feature screening for selecting the most important predictors using "slab and spike" and "local-global shrinkage" priors. We assess the accuracy, precision, and predictive power of the proposed model by simulation studies and analysis of a real-life dataset comparing two standard drugs treating acute lymphocytic leukemia.

Authors who are presenting talks have a * after their name.

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