Based on Kullback-Leibler information we propose a data-driven selector, called GAIC (c) , for choosing parameters of regression splines in nonparametric regression via a stepwise forward/backward knot placement and deletion strategy 1]. This criterion uniies the commonly used information criteria and includes the Akaike information criterion (AIC) 2] and the corrected Akaike information criter...

The statistical information processing can be characterized by the likelihood function de ned by giving an explicit form for an approximation to the true distribution. This mathematical representation, which is usually called a model, is built based on not only the current data but also prior knowledge on the object and the objective of the analysis. Akaike 3) showed that the log-likelihood can...

The relationship between the nuclear parameters and model performance is complex, which is from relevance vector machine (RVM) regression model based on Gaussian radial basis kernel function. Aiming at the problem of how to determine the kernel parameters of RVM, a method to selecting kernel parameter of RVM based on AIC criterion is proposed. Firstly, a novel of statistic Q is proposed based o...

It has been a long time that there is not a so good method to determine the number of neurons in hidden layer for BP neural network. For this problem, a novel algorithm based on Akaike Information Criterion (AIC) to optimize the structure of the BP neuron networks is proposed in this paper. At the same time, this paper gives the upper and lower bounds for classical AIC to overcome its shortcomi...

Akaike s Information Criterion AIC derived from asymptotics of the maximum like lihood estimator is widely used in model selection However it has a nite sample bias which produces over tting in linear regression To deal with this problem Ishiguro et al proposed a bootstrap based extension to AIC which they call EIC In this report we compare model selection performance of AIC EIC a bootstrap smo...

In this paper, we consider the problem of selecting the variables of the fixed effects in the linear mixed models where the random effects are present and the observation vectors have been obtained frommany clusters. As the variable selection procedure, we here use the Akaike Information Criterion, AIC. In the context of the mixed linear models, two kinds of AIC have been proposed: marginal AIC...

The Akaike Information Criterion (AIC) is often used as a measure of model accuracy. The ∆AIC statistic is defined by the difference between AIC values for two nested models. The ∆AIC statistic corresponding to a particular change detection problem has been shown to detect extremely small changes in a dynamic system as compared with traditional change detection monitoring procedures. In this pa...

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...