Statistical models that involve a two-part mixture distribution are applicable in a variety of
situations. Frequently, the two parts are a model for the binary response variable and a …

Two‐part models are quite well established in the economic literature, since they resemble
accurately a principal‐agent type model, where homogeneous, observable, counted …

For count responses, the situation of excess zeros (relative to what standard models allow)
often occurs in biomedical and sociological applications. Modeling repeated measures of …

Non-Gaussian outcomes are often modeled using members of the so-called exponential
family. The Poisson model for count data falls within this tradition. The family in general, and …

Longitudinal or repeated measures data with clumping at zero occur in many applications in
biometrics, including health policy research, epidemiology, nutrition, and meteorology …

Non-Gaussian outcomes are often modeled using members of the so-called exponential
family. Notorious members are the Bernoulli model for binary data, leading to logistic …

In health services research, it is common to encounter semicontinuous data characterized by
a point mass at zero followed by a continuous distribution with positive support. These are …

Count data are collected repeatedly over time in many applications, such as biology,
epidemiology, and public health. Such data are often characterized by the following three …

This paper considers the mixture model methodology for handling non‐ignorable drop‐outs
in longitudinal studies with continuous outcomes. Recently, Hogan and Laird have …

During the past four decades, we have observed a steady increase in the use of repeated
measures data. As the type of data in repeated measures can be discrete or continuous …