The problem of model selection is considerably important for acquiring higher levels of generalization capability in supervised learning. In this paper, we propose a new criterion for model selection called the subspace information criterion (SIC). Computer simulations show that SIC works well even when the number of training examples is small.

This paper presents algorithms for generating random variables for exponential/Rayleigh/ Weibull, Nakagami-m and Rician copulas with any desired copula parameter(s), using the direct conditional cumulative distribution function method and the complex Gaussian distribution method. Moreover, a novel method for optimal copula selection is also proposed, based on the criterion that for a given seri...

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...

The most commonly used model selection criterion, Akaike’s Information Criterion (AIC), cannot be used when the Generalized Estimating Equations (GEE) approach is considered for analyzing multivariate binary response. Recently, a modified version of AIC (mAIC) which is based on quasi-likelihood function is proposed as a model selection criterion. This model selection criterion can be used in th...

The existing failure criteria to evaluate discontinuous rocks are predominatly based on the relations between principal stresses at failure. None of the criteria has a direct reference to the magnitude of deformation as a major parameter in rock Several reasons are identified indicating that the selection of peak stress as a major parameter, which is obtained through the evaluation of a failure...

We propose a new criterion for model selection in prediction problems. The covariance innation criterion adjusts the training error by the average covariance of the predictions and responses, when the prediction rule is applied to permuted versions of the dataset. This criterion can be applied to general prediction problems (for example regression or classiication), and to general prediction ru...

Model or variable selection is usually achieved through ranking models according to the increasing order of preference. One of methods is applying Kullback-Leibler distance or relative entropy as a selection criterion. Yet that will raise two questions, why uses this criterion and are there any other criteria. Besides, conventional approaches require a reference prior, which is usually difficul...

Statistical methods for voice conversion are usually based on a single model selected in order to represent a tradeoff between goodness of fit and complexity. In this paper we assume that the best model may change over time, depending on the source acoustic features. We present a new method for spectral voice conversion called Dynamic Model Selection (DMS), in which a set of potential best mode...