Model selection is commonly based on some variation of the BIC or minimum message length criteria, such as MML and MDL. In either case the criterion is split into two terms: one for the model (data code length/model complexity) and one for the data given the model (message length/data likelihood). For problems such as change detection, unsupervised segmentation or data clustering it is common p...

Contemporary statistical research frequently deals with problems involving a diverging number of parameters. For those problems, various shrinkage methods (e.g., LASSO, SCAD, etc) are found particularly useful for the purpose of variable selection (Fan and Peng, 2004; Huang et al., 2007b). Nevertheless, the desirable performances of those shrinkage methods heavily hinge on an appropriate select...

We evaluate the performance of autoregressive, fractionally integrated, moving average (ARFIMA) modelling for detecting long-range dependence and estimating fractal exponents. More specifically, we test the procedure proposed by Wagenmakers, Farrell, and Ratcliff, and compare the results obtained with the Akaike information criterion (AIC) and the Bayes information criterion (BIC). The present ...

Although Gaussian mixture models are commonly used in acoustic models for speech recognition, there is no standard method for determining the number of mixture components. Most models arbitrarily assign the number of mixture components with little justification. While model selection techniques with a mathematical derivation, such as the Bayesian information criterion (BIC), have been applied, ...

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...

We consider switching regression models with independent or Markov-dependent regime. Based on the modified likelihood ratio test (LRT) statistic by Chen, Chen and Kalbfleisch (2004, JRSSB) we propose a test for two against more states of the underlying regime, and derive its asymptotic distribution in case when there is a single switching parameter. We show that its asymptotic distribution is r...

Radial Basis Function networks with linear output are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. We show how radial base Cauchy, multiquadric and Inverse multiquadric type functions can be used to approximate a rapidly changing continuous test function. In this paper, the performance of the reduced matrix design by QLP decom...

Many statistical models and learning machines which have hierarchical structures, hidden variables, and grammatical rules are not regular but singular statistical models. In singular models, the log likelihood function can not be approximated by any quadratic form of a parameter, resulting that conventional information criteria such as AIC, BIC, TIC or DIC can not be used for model evaluation. ...

Model search strategies play an important role in finding simultaneous susceptibility genes that are associated with a trait. More particularly, model selection via the information criteria, such as the BIC with modifications, have received considerable attention in quantitative trait loci (QTL) mapping. However, such modifications often depend upon several factors, such as sample size, prior d...

background and objective: amygdala contains central nucleus which is the region rich of γ-aminobutyric acid (gaba-a) receptors and plays a key role in modulating behavior and sleep homeostasis. furthermore, dysfunction of amygdala probably contributes to anxiety and mood disorders. in this study, we evaluated the association between acute sleep deprivation (asd) and anxiety through gaba-a recep...