A Note on Estimation of a Distribution Function in a Nonparametric Set-up Using Stein’s Shrinkage Estimation Technique
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Publication:5860272
DOI10.1080/03610918.2013.854908zbMath1474.62108OpenAlexW2063008998MaRDI QIDQ5860272
Nabendu Pal, Manas Ranjan Tripathy
Publication date: 19 November 2021
Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610918.2013.854908
Ridge regression; shrinkage estimators (Lasso) (62J07) Nonparametric estimation (62G05) Admissibility in statistical decision theory (62C15)
Cites Work
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- Inadmissibility of the empirical distribution function in continuous invariant problems
- An approach to improving the James-Stein estimator
- Best invariant estimation of a distribution function under the Kolmogorov-Smirnov loss function
- Estimation of the mean of a multivariate normal distribution
- Improving upon standard estimators in discrete exponential families with applications to Poisson and negative binomial cases
- Minimaxity of the empirical distribution function in invariant estimation
- A sequence of improvements over the James-Stein estimator
- A natural identity for exponential families with applications in multiparameter estimation
- A note on admissibility when precision is unbounded
- Approximation Theorems of Mathematical Statistics
- A Family of Minimax Estimators of the Mean of a Multivariate Normal Distribution