of statistical tests. The models ’ performance can be evaluated using either in-sample or out-of-sample criteria. In the former case, we suggest using the local Kullback-Leibler information criterion, whereas in the latter, we consider the local out-of-sample forecast loss, for a general loss function. We

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Abstract. The problem of automatic image recognition based on the minimum information discrimination principle is formulated and solved. Color histograms comparison in the Kullback–Leibler information metric is proposed. It’s combined with method of directed enumeration alternatives as opposed

Estimation of Kullback-Leibler amount of information is a crucial part of deriving a statistical model selection procedure which is based on likelihood principle like AIC. To discriminate nested models, we have to estimate it up to the order of constant while the Kullback-Leibler information itself

Abstract. An estimator of the Kullback-Leibler information number by using its representation as a functional of the grade density is introduced. Its strong consistency is proved under the mild conditions on the grade density. The same approach is used to study the entropy measure of bivariate

Suppose we have specified a parametric model for the transition distribution of a Markov chain, but that the true transition distribution does not belong to the model. Then the maximum likelihood estimator estimates the parameter which maximizes the Kullback--Leibler information between the true

Based on Kullback-Leibler information we propose a data-driven selector, called GAIC (c) , for choosing parameters of regression splines in nonparametric regression via a stepwise forward/backward knot placement and deletion strategy [1] . This criterion unifies the commonly used information

State space time series clustering using discrepancies based on the Kullback-Leibler information and the Mahalanobis distance

developed for MAT, one based on Fisher information embedded within a Bayesian framework, and the other powered by Kullback-Leibler (KL) information. It is well-known that in unidimensional IRT that the second derivative of KL information (also termed "global information") is Fisher information

Many standard methods for modeling interaction in two way ANOVA require mn interaction parameters, where m and n are the number of rows and columns in the table. By viewing the interaction parameters as a matrix and performing a singular value decomposition, one arrives at the Additive Main Effects and Multiplicative Interaction (AMMI) model which is commonly used in agriculture. By using only those interaction components with the largest singular values, one can produce an estimate of interaction that requires far fewer than mn parameters while retaining most of the explanatory power of standard methods. The central inference problems of estimating the parameters and determining the number of interaction components has been difficult except in "ideal" situations (equal cell sizes, equal variance, etc.). The Bayesian methodology developed in this paper applies for unequal sample sizes and heteroscedastic data, and may be easily generalized to more complicated data structures...