The problem of filtering information from large correlation matrices is of great importance in many applications. We have recently proposed the use of the Kullback–Leibler distance to measure the performance of filtering algorithms in recovering the underlying correlation matrix when the variables are described by a multivariate Gaussian distribution. Here we use the Kullback–Leibler distance t...

In this paper, the problem of variable selection in linear regression is considered. This problem involves choosing the most appropriate model from the candidate models. Variable selection criteria based on estimates of the Kullback-Leibler information are most common. Akaike’s AIC and bias corrected AIC belong to this group of criteria. The reduction of the bias in estimating the Kullback-Leib...

The purpose of this paper is to obtain the tracking interval for difference of expected Kullback-Leibler risks of two models under Type II hybrid censoring scheme. This interval helps us to evaluate proposed models in comparison with each other. We drive a statistic which tracks the difference of expected Kullback–Leibler risks between maximum likelihood estimators of the distribution in two diff...

Motivated from the bandwidth selection problem in local likelihood density estimation and from the problem of assessing a final model chosen by a certain model selection procedure, we consider estimation of the Kullback–Leibler divergence. It is known that the best bandwidth choice for the local likelihood density estimator depends on the distance between the true density and the ‘vehicle’ para...

We propose to compute a sparse approximate inverse Cholesky factor $L$ of dense covariance matrix $\Theta$ by minimizing the Kullback--Leibler divergence between Gaussian distributions $\mathcal{N}(0, \Theta)$ and L^{-\top} L^{-1})$, subject sparsity constraint. Surprisingly, this problem has closed-form solution that can be computed efficiently, recovering popular Vecchia approximation in spat...

In this paper, we develop a general classification framework called Kullback-Leibler Boosting, or KLBoosting. KLBoosting has following properties. First, classification is based on the sum of histogram divergences along corresponding global and discriminating linear features. Second, these linear features, called KL features, are iteratively learnt by maximizing the projected Kullback-Leibler d...

this paper considers the search problem, introduced by srivastava cite{sr}. this is a model discrimination problem. in the context of search linear models, discrimination ability of search designs has been studied by several researchers. some criteria have been developed to measure this capability, however, they are restricted in a sense of being able to work for searching only one possible non...