Identification of proportionality structure with two-part models using penalization

Kuangnan Fang, Xiaoyan Wang, Ben Chang Shia, Shuangge Ma

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Data with a mixture distribution are commonly encountered. A special example is zero-inflated data, where a proportion of the responses takes zero values, and the rest are continuously distributed. Such data routinely arise in public health, biomedicine, and many other fields. Two-part modeling is a natural choice for zero-inflated data, where the first part of the model describes whether the responses are equal to zero, and the second part describes the continuously distributed responses. With two-part models, an interesting problem is to identify the proportionality structure of covariate effects. Such a structure can lead to more efficient estimates and also provide scientific insights into the underlying data-generating mechanisms. To identify the proportionality structure, we adopt a penalization method. Compared to the alternatives, notable advantages of this method include computational simplicity, solid statistical properties, and others. For inference, we adopt a bootstrap approach. The proposed method shows satisfactory performance in simulation and the analysis of two public health datasets.

Original languageEnglish
Pages (from-to)12-24
Number of pages13
JournalComputational Statistics and Data Analysis
Volume99
DOIs
Publication statusPublished - Jul 1 2016

Keywords

  • Penalization
  • Proportionality
  • Two-part modeling
  • Zero-inflated data

ASJC Scopus subject areas

  • Computational Mathematics
  • Computational Theory and Mathematics
  • Statistics and Probability
  • Applied Mathematics

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