Application of Stein-type estimation in combining regression estimates from replicated experiments

This paper considers the application of Stein-type estimation procedure for the coefficients in a linear regression model when data are available from replicated experiment. Two families of estimators characterized by a single scalar are proposed and their large sample asymptotic properties are derived. These are utilized for comparing the performances of the two estimators along with the conventional estimator and conditions for the superiority of one estimator over the other are deduced.     

A fresh approach for intercession of nonresponse in multivariate longitudinal designs

The occurrence of nonresponse is very much plebeian in surveys, which troubles the analysis, and hence, an inappropriate inference is left out. To counterbalance the sour effects of the incompleteness, fresh imputation techniques have been proposed with the aid of multi-auxiliary variates for the estimation of population mean on successive waves. Properties of the proposed estimators have been elaborated, and they have been compared with the work of Priyanka et al. (2015). Detailed simulation study is carried out to substantiate the empirical and theoretical results. Several possible cases have been addressed in which nonresponse can occur.     

Bootstrap method in ranking and slippage problems

In this paper, the bootstrap method of Efron (1979) is given for a ranking and a slippage problem, where the ranking (or slippage) is with respect to the mean of the distributions. The method is also applied to obtain a confidence interval for the largest mean.     

A class of Shrunken estimators for normal mean

This paper presents a class of estimators for the mean of a normal population and determines the conditions on characterizing scalars under which the class of estimators uniformly dominates over the conventional sample mean according to the mean-square-error criterion.     

Local efficiency of moment estimators in beta-binomial model

In this paper, the beta-binomial model is introduced as a Markov chain. It is shown that the correlated binomial model of Kupper and Haseman (1978) is identical to the additive binomial model of AItham(1978) and both are a first order approximation of the beta-binomial model. For small γ, the local efficiency of the moment estimators for the mean ρ and the extra-binomial variation γ is examined analytically. It is shown that, locally, the moment estimator for p is efficient up to the second order of y. Exact formulae for the relative efficiency are obtained for both the cases with γ known and unknown. Generalization to the unequal sample size case is also carried out. In particular, the gain in efficiency by using the quasi-likelihood estimator instead of the ratio estimator for p is studied when γ is known. These results are in agreement with the Monte Carlo results of Kleinman(1973) and Crowder(1985).     

Moments of the function of non-normal random vector with applications to econometric estimators and test statistics 1

In this paper we have provided a general result on the moments of a function of nonnormal random vector. The results for the normal case follow as a special case of this result. It is also indicated that the moments of a large class of econometric estimators and test statistics can be obtained by using our general result. This includes least squares estimator in the dynamic model, unit root tests, and the two step semiparametric estimators, among others.     

Chain ratio type estimator for ratio of two population m3ans using auxiliary characters

In this paper we have proposed chain ratio type estimators for ratio of two population means using two auxiliary characters. The expressions for bias and mean square error of these estimators have been derived. A comparison of the proposed estimator with that of double sampling estimator has been made in terms of mean square error. An emperical study has also been made.     

On vendor part delivery dates in a stochastic assembly system

This paper describes the development of a model for the determination of optimal mean part delivery dates in a stochastic assembly system for the objective of minimizing the expected cost of subassembly and part inventory. Parts are assembled at each station to a subassembly. The part delivery and processing times at assembly stations follow known probability distributions. An approximate solution technique based on the optimization of individual stations in isolation is developed. The approximation applies a correction factor, as a function of the variability in part delivery and processing time, cost parameters and number of stations, to the decisions from the single station solutions to compensate for interdependence between stations. Results indicate that this is an effective approach and yields good near-optimal solutions with very little computational effort. Insights regarding the effect of the type of distribution used, random processing times, variance of the distribution used and cost parameter values on part delivery dates are also reported.     

Reduced Rank Discrimination

In this paper the problem of classifying an individual with p characteristics into one of k multivariate normal distributions with common unknown covariance matrix is considered when the matrix of ( k +1) means has a linear structural relationship, that is, it lies in an r -dimensional plane, where r 相似文献