✦ LIBER ✦

Model selection for generalized linear models with factor-augmented predictors

✍ Scribed by Tomohiro Ando; Ruey S. Tsay

Publisher: John Wiley and Sons
Year: 2009
Tongue: English
Weight: 236 KB
Volume: 25
Category: Article
ISSN: 1524-1904
DOI: 10.1002/asmb.785

No coin nor oath required. For personal study only.

✦ Synopsis

Abstract

This paper considers generalized linear models in a data‐rich environment in which a large number of potentially useful explanatory variables are available. In particular, it deals with the case that the sample size and the number of explanatory variables are of similar sizes. We adopt the idea that the relevant information of explanatory variables concerning the dependent variable can be represented by a small number of common factors and investigate the issue of selecting the number of common factors while taking into account the effect of estimated regressors. We develop an information criterion under model mis‐specification for both the distributional and structural assumptions and show that the proposed criterion is a natural extension of the Akaike information criterion (AIC). Simulations and empirical data analysis demonstrate that the proposed new criterion outperforms the AIC and Bayesian information criterion. Copyright © 2009 John Wiley & Sons, Ltd.

📜 SIMILAR VOLUMES

‘Model selection for generalized linear

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

✍ Hansheng Wang; Chih-Ling Tsai 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 37 KB 👁 1 views

## Discussion 'Model selection for generalized linear models with factor-augmented predictors' Professors Ando and Tsay should be congratulated for such nice work, which provides an effective statistical method to handle high-dimensional data sets with generalized linear models. In this discussio

‘Model selection for generalized linear

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

✍ T. Ando; R. S. Tsay 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 56 KB 👁 1 views

We would like to thank all the discussants for their wide-ranging discussions and constructive suggestions. They provide many useful references and directions for further research. We organize our reply in sections and hope that they can attract more discussions and encourage deeper research and dev

‘Model selection for generalized linear

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

✍ W. K. Li; Guodong Li 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 48 KB

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 num

Selection of Log-Linear Models with Orth

Selection of Log-Linear Models with Orthonormal Basis for Contingency Tables

✍ Fred Böker 📂 Article 📅 2007 🏛 John Wiley and Sons 🌐 English ⚖ 371 KB

A model aelection criterion for log-linear models with orthonormal basis for contingency tablea is developed ueing the Gauee discrepancy between the logarithms of'the freqnenciea. The wntribution of each parameter to the criterion may be determined eeprately. A teet for the hypothesis that the w e o

Generalized Broadwell models for the dis

Generalized Broadwell models for the discrete Boltzmann equation with linear and quadratic terms

✍ Mitsuru Yamazaki 📂 Article 📅 2000 🏛 John Wiley and Sons 🌐 English ⚖ 97 KB

A generalization of the Broadwell models for the discrete Boltzmann equation with linear and quadratic terms is investigated. We prove that there exists a time-global solution to this model in one space-dimension for locally bounded initial data, using a maximum principle of solutions. The boundedne

On estimating equations for parameters i

On estimating equations for parameters in generalized linear mixed models with application to binary data

✍ Roy F. Bartlett; Brajendra C. Sutradhar 📂 Article 📅 1999 🏛 John Wiley and Sons 🌐 English ⚖ 168 KB 👁 2 views

In view of the cumbersome and often intractable numerical integrations required for a full likelihood analysis, several suggestions have been made recently for approximate inference in generalized linear mixed models and other nonlinear variance component models. For example, we refer to the penaliz