Parametric empirical Bayes PEB may perform poorly when the assumed prior distribution is seriously invalid Nonparametric empirical Bayes NEB is more robust since it imposes no restric tion on the prior But compared with the PEB the NEB may be ine cient for small to medium samples due to the large variation and under dispersion of the NPMLE of the prior Using Monte Carlo simulations we compare t...

The Borel-Tanner probability distribution was derived by Borel (1942) and Tanner (1953) to characterize the distribution behavior of the number of customers served in a queuing system with Poisson input and constant service time. Later this probability distribution was applied in some models for random trees and branching processes. In the latter case one of the parameters can be interpreted as...

An impressive array of paper has been devoted to the reliability properties and the hazard rates of the order statistics along with IFR (increasing failure rate) and DFR (decreasing failure rate) property. However, studies relating to the Bayesian estimation on the repairable system along with IFR property of the failure time distribution and on the repair time distributions have received compa...

Data mining applications require learning algorithms to have high predictive accuracy, scale up to large datasets, and produce compre-hensible outcomes. Naive Bayes classiier has received extensive attention due to its eeciency, reasonable predictive accuracy, and simplicity. However , the assumption of attribute dependency given class of Naive Bayes is often violated, producing incorrect proba...

This work presents a new general purpose classifier named Averaged Extended Tree Augmented Naive Bayes (AETAN), which is based on combining the advantageous characteristics of Extended Tree Augmented Naive Bayes (ETAN) and Averaged One-Dependence Estimator (AODE) classifiers. We describe the main properties of the approach and algorithms for learning it, along with an analysis of its computatio...

Wavelet methods have demonstrated considerable success in function estimation through term-by-term thresholding of the empirical wavelet coefficients. However, it has been shown that grouping the empirical wavelet coefficients into blocks and making simultaneous threshold decisions about all the coefficients in each block has a number of advantages over term-by-term wavelet thresholding, includ...

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds’ algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning...

From the first appearance of network attacks, the internet worm, to the most recent one in which the servers of several famous e-business companies were paralyzed for several hours, causing huge financial losses, network-based attacks have been increasing in frequency and severity. As a powerful weapon to protect networks, intrusion detection has been gaining a lot of attention. Traditionally, ...

In this paper we consider the nonparametric functional estimation of the drift of Gaussian processes using Paley-Wiener and Karhunen-Loève expansions. We construct efficient estimators for the drift of such processes, and prove their minimaxity using Bayes estimators. We also construct superefficient estimators of Stein type for such drifts using the Malliavin integration by parts formula and s...