To address order selection for an autoregressive model fitted to time series data, we propose a new information criterion. It has the benefits of the two wellknown model selection techniques, the Akaike information criterion and the Bayesian information criterion. When the data is generated from a finite order autoregression, the Bayesian information criterion is known to be consistent, and so ...

In this paper, we propose a clustering method by SOM and information criteria. In this method, initial cluster-candidates are derived by SOM, and then these candidates are merged appropriately based on information criterion such as BIC or AIC (Akaike Information Criterion). Through the clustering experiments for the artificial datasets and UCI Machine Learning Repository’s datasets, we confirm ...

Bayesian network (BN) modeling has recently been introduced as a tool for determining the dependencies between brain regions from functional-magnetic-resonance-imaging (fMRI) data. However, studies to date have yet to explore the optimum way for meaningfully combining individually determined BN models to make group inferences. We contrasted the results from three broad approaches: the "virtual-...

Current advances in observational cosmology suggest that our Universe is flat and dominated by dark energy. There are several different theoretical ideas invoked to explain the dark energy with relatively little guidance of which one of them might be right. Therefore the emphasis of ongoing and forthcoming research in this field shifts from estimating specific parameters of cosmological model t...

Bayesian networks are among most popular model classes for discrete vector-valued i.i.d data. Currently the most popular model selection criterion for Bayesian networks follows Bayesian paradigm. However, this method has recently been reported to be very sensitive to the choice of prior hyper-parameters [1]. On the other hand, the general model selection criteria, AIC [2] and BIC [3], are deriv...

The limited range in its abscissa of ranked letter frequency distributions causes multiple functions to fit the observed distribution reasonably well. In order to critically compare various functions, we apply the statistical model selections on ten functions, using the texts of U.S. and Mexican presidential speeches in the last 1-2 centuries. Dispite minor switching of ranking order of certain...

Model-selection criteria such as AIC and BIC are widely used in applied statistics. In recent years, there has been a huge increase in modeling data from large complex surveys, and a resulting demand for versions of AIC and BIC that are valid under complex sampling. In this paper, we show how both criteria can be modified to handle complex samples. We illustrate with two examples, the first usi...