Recently, various types of mixture models have been developed for data sets having a hierarchical or multilevel structure (see, e,g., [9, 12]). Most of these models include finite mixture distributions at multiple levels of a hierarchical structure. In these multilevel mixture models, selection of the number of mixture component is more complex than in standard mixture models because one has to...

This paper addresses the problem of learning Bayesian network structures from data based on score functions that are decomposable. It describes properties that strongly reduce the time and memory costs of many known methods without losing global optimality guarantees. These properties are derived for different score criteria such as Minimum Description Length (or Bayesian Information Criterion)...

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...

The competing risks model is useful in settings in which individuals (or units) may die (or fail) due to a number of different causes. It can also be the case that for some of the items the failure cause is known only up to a subgroup of all causes in which case we say that the failure is group masked. A widely used approach for competing risks data with and without masking involves the specifi...

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...

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 ...

Consider the simple normal linear regression model for estimation/prediction at a new design point. When the slope parameter is not obviously nonzero, hypothesis testing and model selection methods can be used for identifying the right model. We compare performance of such methods both theoretically and empirically from different perspectives for more insight. The testing approach, in spite of ...

A simple theoretical framework for the description and interpretation of spatially correlated modelling residuals is presented, and the resulting tools are found to provide a useful aid to model selection in the context of weak gravitational lensing. The description is focused upon the specific problem of modelling the spatial variation of a telescope point spread function (PSF) across the inst...