# Journal of Statistical Theory and Practice

Journal of Statistical Theory and Practice > 2008 > 2 > 1 > 109-123

Journal of Statistical Theory and Practice > 2008 > 2 > 1 > 55-69

*OLS*) variance-covariance matrix estimator for a linear regression model under heteroscedastic errors is biased and inconsistent. Accordingly, several estimators have so far been proposed by various researchers. However, none of these perform well under the finite-sample situation. In this paper, the powerful optimization technique of Genetic algorithm (

*GA*)...

Journal of Statistical Theory and Practice > 2008 > 2 > 1 > 15-19

Journal of Statistical Theory and Practice > 2008 > 2 > 1 > 45-54

Journal of Statistical Theory and Practice > 2008 > 2 > 1 > 21-43

Journal of Statistical Theory and Practice > 2008 > 2 > 1 > 71-81

Journal of Statistical Theory and Practice > 2008 > 2 > 1 > 83-94

Journal of Statistical Theory and Practice > 2008 > 2 > 1 > 1-14

Journal of Statistical Theory and Practice > 2008 > 2 > 1 > 95-108

*t.q*

*ζ*

*∥*

*n*, where

*ζ*factors are at

*q*-levels each and one factor is at

*t*-levels and the number of runs is

*n*. The designs obtained have the...

Journal of Statistical Theory and Practice > 2008 > 2 > 2 > 199-219

Journal of Statistical Theory and Practice > 2008 > 2 > 2 > 221-232

Journal of Statistical Theory and Practice > 2008 > 2 > 2 > 159-172

Journal of Statistical Theory and Practice > 2008 > 2 > 2 > 279-292

*T*is split into

*M*subtasks of lengths

*T*

_{1}, …,

*T*

_{M}, each of which is sent to one out of

*M*parallel processors. Each processor may fail at a random time before completing its allocated task, and then has to restart it from the beginning. If

*X*

_{1}, …,

*X*

_{M}are the total task times at the

*M*processors, the overall total task time is then

*Z*

_{M}= max

_{1,…,M}

*X*

_{i}. Limit theorems as

*M*→ ∞ are given...

Journal of Statistical Theory and Practice > 2008 > 2 > 2 > 183-197

Journal of Statistical Theory and Practice > 2008 > 2 > 2 > 173-182

Journal of Statistical Theory and Practice > 2008 > 2 > 2 > 145-158

Journal of Statistical Theory and Practice > 2008 > 2 > 2 > 293-326

*μ*

_{λ}= Σ

_{x}ξ

_{x}δ

_{x}where the sum is over points

*x*of a Poisson point process of intensity

_{λ}on a bounded region in

*d*-space, and ξ

_{x}is a functional determined by the Poisson points near to

*x*, i.e. satisfying an exponential stabilization condition, along with a moments condition (examples include statistics for proximity graphs, germ-grain models and random sequential deposition models)...