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Activity Number: 521 - Model/Variable Selection
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #327153 Presentation
Title: Penalized Multiple Inflated Values Selection Method with Application to SAFER Data
Author(s): Qiuya Li* and Kwok Fai TSO and Yang Li and Yichen Qin and Travis Lovejoy and Timothy Heckman
Companies: City University of Hong Kong and City University of Hong Kong and Renmin University of China and University of Cincinnati and Oregon Health and Science University and University of Georgia
Keywords: adaptive LASSO; count data; inflated values selection; mixture model; multiple inflation

Expanding on the zero-inflated Poisson (ZIP) model, the multiple-inflated Poisson (MIP) model is applied to analyze count data with multiple inflated values. The existing studies on the MIP model determined the inflated values by inspecting the histogram of count response and fitting the model with different combinations of inflations, which leads to relatively complicated computations and may overlook some real inflated points. We address a two-stage inflated values selection method, which takes all values of count response as potential inflated values and adopts the adaptive lasso regularization on the mixing proportion of those inflated values. Numerical studies demonstrate the excellent performance both on inflated values selection and parameters estimation. Moreover, a specially designed simulation, based on the structure of data from a randomized clinical trial of an HIV sexual risk education intervention, performs well and ensures our method could be generalized to the real situation. The empirical analysis of a clinical trial dataset is used to elucidate the MIP model.

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

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