TY - JOUR

T1 - Identification of proportionality structure with two-part models using penalization

AU - Fang, Kuangnan

AU - Wang, Xiaoyan

AU - Shia, Ben Chang

AU - Ma, Shuangge

PY - 2016/7/1

Y1 - 2016/7/1

N2 - 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.

AB - 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.

KW - Penalization

KW - Proportionality

KW - Two-part modeling

KW - Zero-inflated data

UR - http://www.scopus.com/inward/record.url?scp=84957990953&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84957990953&partnerID=8YFLogxK

U2 - 10.1016/j.csda.2016.01.002

DO - 10.1016/j.csda.2016.01.002

M3 - Article

AN - SCOPUS:84957990953

VL - 99

SP - 12

EP - 24

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

SN - 0167-9473

ER -