James-Stein Estimator: Introduction

2019 ◽  
pp. 1-4
Author(s):  
Benjamin R. Baer ◽  
Martin T. Wells ◽  
George Casella
Keyword(s):  
Stein Estimator

scholarly journals Estimation of the Number of Unemployed and Employed For Lithuanian Counties and Municipalities Using Models

2015 ◽  
Vol 54 (1) ◽  
pp. 33-44
Author(s):  
Linas Naujanis ◽  
Danutė Krapavickaitė
Keyword(s):  
Standard Deviation ◽  
Sampling Design ◽  
Finite Population ◽  
Labour Force ◽  
Parameters Estimation ◽  
Population Parameters ◽  
Model Based ◽  
Stein Estimator ◽  
Role Of Information

Problems of finite population parameters estimation are analyzed in this paper. Four methods have been used for parameterestimation: sampling design-based unbiased estimator, multiple regression and logistic regression model-based estimators and James–Stein estimator. The design-based estimator is unbiased, but its standard deviation is usually high. Model-based estimators are notunbiased, but their standard deviations are low. In order to minimize the standard deviation and the bias, the James–Stein estimator isapplied. Labour force survey data of Statistics Lithuania are used for simulation to study model-based estimators for the number ofunemployed and employed persons in districts and counties, and the role of information on registered unemployment in these models.

Estimators which are Uniformly Better than the James-Stein Estimator

1997 ◽  
Vol 47 (3-4) ◽  
pp. 167-180 ◽  
Author(s):  
Nabendu Pal ◽  
Jyh-Jiuan Lin
Keyword(s):  
Maximum Likelihood ◽  
Normal Distribution ◽  
Maximum Likelihood Estimator ◽  
Multivariate Normal Distribution ◽  
Multivariate Normal ◽  
Likelihood Estimator ◽  
Mean Estimation ◽  
Stein Estimator ◽  
Mean Vector ◽  
Better Than

Assume i.i.d. observations are available from a p-dimensional multivariate normal distribution with an unknown mean vector μ and an unknown p .d. diaper- . sion matrix ∑. Here we address the problem of mean estimation in a decision theoretic setup. It is well known that the unbiased as well as the maximum likelihood estimator of μ is inadmissible when p ≤ 3 and is dominated by the famous James-Stein estimator (JSE). There are a few estimators which are better than the JSE reported in the literature, but in this paper we derive wide classes of estimators uniformly better than the JSE. We use some of these estimators for further risk study.

Note on the applicability of the James-Stein Estimator in regional hydrologic studies

1984 ◽  
Vol 20 (11) ◽  
pp. 1630-1638 ◽  
Author(s):  
J. Maciunas Landwehr ◽  
N. C. Matalas ◽  
J. R. Wallis
Keyword(s):  
Stein Estimator

scholarly journals Limits of Risks Ratios of Shrinkage Estimators under the Balanced Loss Function

2021 ◽  
pp. 301-312
Author(s):  
Mekki Terbeche
Keyword(s):  
Maximum Likelihood ◽  
Loss Function ◽  
Sufficient Conditions ◽  
Maximum Likelihood Estimators ◽  
Shrinkage Estimators ◽  
Multivariate Normal ◽  
Balanced Loss Function ◽  
Multivariate Normal Mean ◽  
Normal Mean ◽  
Stein Estimator

In this paper we study the estimation of a multivariate normal mean under the balanced loss function. We present here a class of shrinkage estimators which generalizes the James-Stein estimator and we are interested to establish the asymptotic behaviour of risks ratios of these estimators to the maximum likelihood estimators (MLE). Thus, in the case where the dimension of the parameter space and the sample size are large, we determine the sufficient conditions for that the estimators cited previously are minimax

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