Identification of proportionality structure with two-part models using penalization

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

Computational Statistics & Data Analysis, 2016, vol. 99, issue C, 12-24

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.

Keywords: Zero-inflated data; Two-part modeling; Proportionality; Penalization (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:99:y:2016:i:c:p:12-24

DOI: 10.1016/j.csda.2016.01.002

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