In this paper, we define two restricted estimators for the regression parameters in a multiple linear regression model with measurement errors when prior information for the parameters is available. We then construct two sets of improved estimators which include the preliminary test estimator, the Stein-type estimator and the positive rule Stein type estimator for both slope and intercept, and ...

‎Zero-inflated negative binomial model is an appropriate choice to count response variables with excessive zeros and over-dispersion simultaneously. ‎This paper addressed parameter estimation in the zero-inflated when there are many parameters, ‎so that some of them have not influence on variable. ‎We proposed based linear shrinkage, ‎pretest, ‎shrinkage pretest, ‎Stein-type, ‎and positive Stei...

Robert Stein is the Mildred and Ernest Hogan Professor of New Testament at The Southern Baptist Theological Seminary. Dr. Stein has a Ph.D. from Princeton Theological Seminary, taught at Bethel College and Bethel Theological Seminary from 1969-1997, and has served as a Professor at Southern since 1997. He is a renowned scholar and has writ ten numerous books, articles, and book reviews. His mos...

Background. An important problem in molecular biology is to determine the complete transcription profile of a single cell, a snapshot that shows which genes are being expressed and to what degree. Seen in series as a movie, these snapshots would give direct, specific observation of the cell’s regulation behavior. Taking a snapshot amounts to correctly classifying the cell’s ∼300 000 mRNA molecu...

A new class of minimax Stein-type shrinkage estimators of a multivariate normal mean is studied where the shrinkage factor is based on an `p norm. The proposed estimators allow some but not all coordinates to be estimated by 0 thereby allow sparsity as well as minimaxity. AMS 2000 subject classifications: Primary 62C20; secondary 62J07.

In unidentifiable models, the Bayes estimation has the advantage of generalization performance over the maximum likelihood estimation. However, accurate approximation of the posterior distribution requires huge computational costs. In this paper, we consider an alternative approximation method, which we call a subspace Bayes approach. A subspace Bayes approach is an empirical Bayes approach whe...