ABSTRACT In the present paper, we consider the variable selection problem in Poisson regression models. Akaike’s information criterion (AIC) is the most commonly applied criterion for selecting variables. However, the bias of the AIC cannot be ignored, especially in small samples. We herein propose a new bias-corrected version of the AIC that is constructed by stochastic expansion of the maximu...

In the mixed modeling framework, Monte Carlo simulation and cross validation are employed to develop an “improved” Akaike information criterion, AICi, and the predictive divergence criterion, PDC, respectively, for model selection. The selection and the estimation performance of the criteria is investigated in a simulation study. Our simulation results demonstrate that PDC outperforms AIC and A...

SUBSET, written in the matrix language Gauss, is a program that identifies optimal subsets of means or proportions based on independent groups. All possible configurations of ordered subsets of groups are identified and the best model is selected using either the AIC or BIC information criterion. For means, both homogeneous and heterogeneous variance cases are considered. SUBSET offers an alter...

An intensive simulation study to compare the spatio–temporal prediction performances among various space–time models is presented. The models having separable spatio–temporal covariance functions and nonseparable ones, under various scenarios, are also considered. The computational performance among the various selected models are compared. The issue of how to select an appropriate space–time m...

The Cp selection criterion is a popular method to choose the smoothing parameter in spline regression. Another widely used method is the generalized maximum likelihood (GML) derived from a normal-theory empirical Bayes framework. These two seemingly unrelated methods, have been shown in Efron (Ann. Statist. 29 (2001) 470) and Kou and Efron (J. Amer. Statist. Assoc. 97 (2002) 766) to be actually...

A major analytical challenge in computational biology is the detection and description of clusters of specified site types, such as polymorphic or substituted sites within DNA or protein sequences. Progress has been stymied by a lack of suitable methods to detect clusters and to estimate the extent of clustering in discrete linear sequences, particularly when there is no a priori specification ...

Learning visual context is a critical step of dynamic scene modelling. This paper addresses the problem of choosing the most suitable probabilistic model selection criterion for learning visual context of a dynamic scene. A Completed Likelihood Akaike’s Information Criterion (CL-AIC) is formulated to estimate the optimal model order (complexity) for a given visual scene. CL-AIC is designed to o...

Experimental data need to be assessed for purposes of model identification, estimation of model parameters and consequences of misspecified model fits. Here the first and third factors are considered via analytic formulations for the distribution of the maximum likelihood estimates. When estimating this distribution with statistics, it is a tradition to invert the roles of population quantities...

Dependent Variable: Y Method: Least Squares Date: 05/23/00 Time: 05:55 Sample: 1 33 Included observations: 33 Variable Coefficient Std. Error t-Statistic Prob. C 102192.4 12799.83 7.983891 0.0000 N -9074.674 2052.674 -4.420904 0.0001 P 0.354668 0.072681 4.879810 0.0000 I 1.287923 0.543294 2.370584 0.0246 R-squared 0.618154 Mean dependent var 125634.6 Adjusted R-squared 0.578653 S.D. dependent v...

Model selection is the problem of distinguishing competing models, perhaps featuring different numbers of parameters. The statistics literature contains two distinct sets of tools, those based on information theory such as the Akaike Information Criterion (AIC), and those on Bayesian inference such as the Bayesian evidence and Bayesian Information Criterion (BIC). The Deviance Information Crite...