A class of general pretest estimators for the univariate normal mean
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Publication:6106210
DOI10.1080/03610926.2021.1955384OpenAlexW3191278913MaRDI QIDQ6106210
Yoshihiko Konno, Takeshi Emura, Yuan-Tsung Chang, Jia-Han Shih
Publication date: 27 June 2023
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://figshare.com/articles/journal_contribution/A_class_of_general_pretest_estimators_for_the_univariate_normal_mean/15132080
Cites Work
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- Some comments on Statistical Papers 46, 379-395 (2005)
- Estimation of the intercept parameter for linear regression model with uncertain non-sample prior information
- Robust ridge M-estimators with pretest and Stein-rule shrinkage for an intercept term
- An efficient class of estimators for the mean of a finite population in two-phase sampling using multi-auxiliary variates
- Biased estimation in a simple multivariate regression model
- Statistical decision theory and Bayesian analysis. 2nd ed
- A note on the noncentral chi-square distribution
- An MSE comparison of the restricted Stein-rule and minimum mean squared error estimators in regression
- Bayesian nonparametric multiple testing
- Survival analysis with correlated endpoints. Joint frailty-copula models
- A high-dimensional spatial rank test for two-sample location problems
- Penalty, shrinkage and pretest strategies. Variable selection and estimation
- The Traditional Pretest Estimator
- Shrinkage confidence intervals for the normal mean: Using a guess for greater efficiency
- Specification Tests in Econometrics
- Two-stage james-stein estimators of the mean based on prior knowledge
- Mathematical Statistics
- Minimax Regret Significance Points for a Preliminary Test in Regression Analysis
- On Biases in Estimation Due to the Use of Preliminary Tests of Significance
- On the comparison of the pre-test and shrinkage estimators for the univariate normal mean
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