The jackknife procedure for the probabilities of misclassification

Seven estimators for the probabilities of misclassifi-cation associated with the linear discriminant function are considered. Four of them are known in the literature. The remaining three are constructed through the Jackknife Pro-cedure. An empirical investigation is conducted to evalu-ate the relative merits of these estimators. Summary of the results is presented.     

Bivariate dispersion quality control charts

A Shewhart procedure is used to simultaneously control the standard deviations of quality characteristics assumed to have a bivariate normal distribution. Following Krishnaiah et al (1963), we use the bivariate chi-square distribution to determine probabilities of out-of-control signals and thus the respective average run lengths (ARLs). Results from an example indicate that for both one-sided and two-sided cases, signals occur only slightly more quickly for changes in the process standard deviations for uncorrected variables than for correlated variables.     

Estimation for two-way analysis of variance with correlated errors

We consider some estimation and distribution problems encountered in a two way analysis of variance model with only one observation per cell, errors correlated in one level, and the variances are not necessarily equal. The independence criteria for the row and interaction mean sum of squares and distribution of the maximum likelihood estimator of the correlation coefficient are given.     

Expectations of the maximum of sums and the sum of maxima for correlated normal variables

This paper compares expectations of the maximum of sums and the sum of maxima for correlated normal variables. General formulas are given for the expected differences and relative expected differences. Numerical values are computed for the special case of a correlated structure with common variance and covariance to illustrate the general magnitude of the differences which may occur. It is numerically shown that an approximation to the relative differences based on the extreme value distribution is very good. General correlated models with location-scale parameters are also considered. This result should be useful in application. Examples are discussed.     

Acceptance sampling plans by variables for a class of symmetric distributions

The estimation of percentage defectives using a normal sampling plan will not be appropriate when the assumption of normality is violated. In this paper, we propose a sampling plan based on a more general symmetric family of distributions with the parameters estimated using the modified maximum likelihood (MML) procedures introduced by Tiku and Suresh . This sampling plan works well for most of the symmetric non-normal distributions. Some numerical study has also been carried out to show the superiority of the proposed plan.     

The effect of rounding on sequential and fixed size sample hypothesis tests

In previous literature the effects of rounding on fixed sample hypothesis tests have been considered. However sequential tests have received little attention. In this paper the robustness of these tests under rounding is considered. The results indicate that in terms of significance level and power the sequential tests were less affected by rounding than the fixed sample tests.     

On the distribution of a second degree polynomial statistic in singular normal var1aies

This note provides an alternative proof of a basic theorem on the distribution of a second degree polynomial statistic in noncentral singular normal variates originally proven by Rayner and Livingstone (1965)     

A two stage procedure for selecting δ∗-optimal means in the normal model

Suppose π₁,…,π_k are k normal populations with π_i having unknown mean μ_i and unknown variance σ². The population π_i will be called δ^?-optimal (or good) if μ_i is within a specified amountδ? of the largest mean. A two stage procedure is proposed which selects a subset of the k populations and guarantees with probability at least P^? that the selected subset contains only δ?-optimal π_i ’s. In addition to screening out non-good populations the rule guarantees a high proportion of sufficiently good π_i’S will be selected.     

On the Computation of the Distribution for the Analysis of Means

Abstract

We discuss the accuracy of the computation and present a fortran program to compute the cumulative distribution function (CDF) for the analysis of means (ANOM).     

Improved estimation of the mean vector for student-t model

Improved James-Stein type estimation of the mean vector μ of a multovaroate Student-t population of dimension p with ν degrees of freedom is considered. In addition to the sample data, uncertain prior information on the value of the mean vector, in the form of a null hypothesis, is used for the estiamtion. The usual maximum liklihood estimator((mle) of μ is obtained and a test statistic for testing H₀:μ=μ₀ is derived. Based on the mle of μ and the tes statistic the preliminary test estimator (PTE), Stein-type shrinkage estimator (SE) and positive-rule shrinkage esiimator (PRSE) are defined. The bias and the quadratic risk of the estimators are evaiuated. The relative performances of the estimators are mvestigated by analyzing the risks under different condltlons It is observed that the FRSE dommates over he other three estimators, regardless of the vaiidity of the null hypothesis and the value ν.