We propose a new method in two variations for the identification of most relevant covariates in linear models with homoscedastic errors. In contrast to AIC, BIC and other information criteria, our method is based on an interpretable scaled quantity. This quantity measures a maximal relative error one makes by selecting covariates from a given set of all available covariates. The proposed model ...

While the classical framework has a rich set of limited information procedures such as GMM and other related methods, the situation is not so in the Bayesian framework. We develop a limited information procedure in the Bayesian framework that does not require the knowledge of the full likelihood. The developed procedure is a Bayesian counterpart of the classical GMM but has advantages over the ...

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...

We propose a flexible change-point model for inhomogeneous Poisson Processes, which arise naturally from next-generation DNA sequencing, and derive score and generalized likelihood statistics for shifts in relative intensity functions. We construct a modified Bayesian information criterion (mBIC) to guide model selection, and point-wise approximate Bayesian confidence intervals for assessing th...

This work addresses the question of modeling the stress contours of Brazilian and Modern European Portuguese as high order Markov chains. We discuss three criteria to select the order of the chain: the Akaike’s Information Criterion, the Bayesian Information Criterion and the Minimum Entropy Criterion. A statistical analysis of a sample of spontaneous speech from both dialects indicates that th...

In Minimum Message Length (MML) clustering (unsupervised classification, mixture modelling) the aim is to infer a set of classes that best explains the observed data items. There are cases where parts of the observed data do not need to be explained by the inferred classes but can be used to improve the inference and resulting predictions. Our main contribution is to provide a simple and flexib...

Abstract. Many examples of calibration in climate science raise no alarms regarding model reliability. We examine one example and show that, in employing Classical Hypothesis-testing, it involves calibrating a base model against Many examples of calibration in climate science raise no alarms regarding model reliability. We examine one example and show that, in employing Classical Hypothesis-tes...

Information criteria are an appropriate and widely used tool for solving model selection problems. However, different ways to use them exist, each leading to a more or less precise approximation of the sought model. In this paper, we mainly present two methods of utilisation of information criteria : the classical one which is generally used and an alternative one, more precise but requiring a ...

[1] Tsai and Li [2008] assert that the Bayesian information criterion (BIC) [Schwarz, 1978] is better suited for comparing models having different parameters than is the Kashyap criterion (KIC) [Kashyap, 1982] because a Fisher information term in the latter may rank models with relatively large parameter estimation uncertainties higher than other models. We start by noting that KIC reduces asym...

To determine the number of clusters in the clustering analysis that has a broad range of applied sciences, such as physics, chemistry, biology, engineering, economics etc., many methods have been proposed in the literature. The aim of this paper is to determine the number of clusters of a dataset in a model-based clustering by using an Analytic Hierarchy Process (AHP). In this study, the AHP mo...