A recursive approach for lot sentencing problem in the presence of inspection errors

In this paper, a new sequential acceptance sampling plans in the presence of inspection errors is developed. A suitable profit objective function is employed for optimizing the lot sentencing problem. A backward recursive approach is applied for obtaining the profit of different decisions in each stage of sampling. Required probabilities are obtained using Bayesian rule. A case study is solved for illustrating the application of proposed models and sensitivity analysis are carried out on the parameters of the proposed methodologies and the behaviour of models by changing the parameters are investigated.     

A class of distributions connected to order statistics with nonintegral sample size

Newton's binomial series expansion is used to develop a (class of) distribution function(s) F_r:∝ which may be interpreted as the distribution of the r^thorder statistic with nonintegral sample size∝. It is shown that F_r:∝ has properties similar to F_r:n. Multivariate extension is considered and an elementary proof of the integral representation for the joint distribution of a subset of order statistics is given. An application is included.     

Estimation of Slope for Linear Regression Model with Uncertain Prior Information and Student-t Error

This article considers estimation of the slope parameter of the linear regression model with Student-t errors in the presence of uncertain prior information on the value of the unknown slope. Incorporating uncertain non sample prior information with the sample data the unrestricted, restricted, preliminary test, and shrinkage estimators are defined. The performances of the estimators are compared based on the criteria of unbiasedness and mean squared errors. Both analytical and graphical methods are explored. Although none of the estimators is uniformly superior to the others, if the non sample information is close to its true value, the shrinkage estimator over performs the rest of the estimators.     

On a further generalization of the savage test

A statistic based on the frequencies within the k+1 intervals specified by k arbitrary quantiles is proposed for a LMP test against Lehmann alternatives generalizing the Savage test for the two-sample problem. The maximum efficiency relative to the Savage test for optimally chosen k quantiles is also provided for k=l(2)l5. The asymptotic normality of the statistic follows from the asymptotic multinomial distribution of the frequencies in the classes determined by the k quantiles.     

Estimating quantiles using optimally selected order statistics

This expository paper deals with the linear estimation of quantiles of location-scale families of distributions using a few selected order statistics.The general theory for the problem i s reviewed for the exact as well as the asymptotic cases.     

On shrinkage r-estimation in a multiple regression model

For a multiple regression model, bearing the plausibility of a subset of the regression parameters being close to a pivot, for the complementary subset, based on the usual James-Stein rule, a general formulation of shrinkage R-estimation is considered. In the light of asymptotic distributional risks of estimators, performance characteristics ( under local alternatives) of the classical R-est-imator and its preliminary test and shrinkage versions (all based on the common score function ) are studied. These shed light on the relative dominance picture in a meaningful asymptotic setup.     

Estimation of parameters of location-scale family of distributions in complete and type ii censored samples

In this paper we consider the estimation of location, scale and quantile-function of a location-scale family of distributions in complete and Type II multicensored samples. Our method integrates the two well-known approaches to probability plotting, namely, the analytic method of Blom (1958) and the hazard plotting method due to Nelson (1972). Some important special cases of location-scale family of distributions are considered for details.     

Tests of homogeneity of intercepts after a preliminary test

For the multl-sample regression model , the problem of testing equality of intercepts following a preliminarytest on the equality o f slopes is considered. The t e s t s are based on the conventional least squares estimators (without necessarily assuming the normality of the underlying error distributions ) , and the impact of a

preliminary test on the asymptotic size and power of the final test is studied.