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‘Model selection for generalized linear models with factor-augmented predictors’

✍ Scribed by W. K. Li; Guodong Li

Publisher
John Wiley and Sons
Year
2009
Tongue
English
Weight
48 KB
Volume
25
Category
Article
ISSN
1524-1904

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✦ Synopsis

We congratulate the authors for contributing such an innovative paper in terms of both modeling methodology and subject matter significance. This paper enriches the application of generalized linear models in a data-rich environment. The principal component method was first applied to reduce the number of covariates, and an information criterion was derived to select the number of common factors. Here we would like to consider the model from two alternative viewpoints, time series data and zero-inflated data.

Zeger and Qaqish [1] first considered autoregressive time series models in the framework of generalized linear models, and Li [2] further extended their results to the commonly used autoregressive moving average (ARMA) cases, the so-called generalized ARMA or GARMA models. This model has recently attracted much attention, e.g. binary models in biology [3] and binomial models in economics [4]. However, high-dimensional data are also usually encountered in the literature. It should be of interest to consider such type of models with factoraugmented predictors. For a time series {y t } with covariates {X t }, denote the information set H t = {X t , .

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