Universal domination and stochastic domination: Estimation simultaneously under a broad class of loss functions
From MaRDI portal
Publication:1065468
DOI10.1214/aos/1176346594zbMath0577.62007OpenAlexW2022092151MaRDI QIDQ1065468
Publication date: 1985
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1214/aos/1176346594
loss functionmultivariate t distributionmultivariate normalstochastic dominationJames-Stein positive part estimatorsuniversal domination
Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) General considerations in statistical decision theory (62C05)
Related Items (43)
EFFICIENCY OF LINEAR ESTIMATORS UNDER HEAVY-TAILEDNESS: CONVOLUTIONS OF [alpha-SYMMETRIC DISTRIBUTIONS] ⋮ Estimation of Regression Coefficients in a Restricted Measurement Error Model Using Instrumental Variables ⋮ On intra-class correlation coefficient estimation ⋮ On the maximum likelihood estimator under order restrictions in uniform probability models ⋮ A class of closeness criteria ⋮ Confidence interval estimation of population means subject to order restrictions using resampling procedures ⋮ Pitman closeness properties of Bayes shrinkage procedures in estimation and prediction ⋮ Universal admissibility in second order asymptotics ⋮ Simultaneous estimation in a restricted linear model ⋮ Optimality of the least squares estimator ⋮ Universal domination of stein-type estimators ⋮ On estimation of the common mean of \(k\;(\geq 2)\) normal populations with order restricted variances ⋮ Median loss decision theory ⋮ Modifying estimators of ordered positive parameters under the Stein loss ⋮ Reduction of risk using restricted estimators ⋮ A note on universal admissibility of scale parameter estimators ⋮ Possibility distributions: a unified representation of usual direct-probability-based parameter estimation methods ⋮ On the coefficient of multiple determination in a linear regression model ⋮ Improved estimators for the common means of two normal distributions with ordered variances ⋮ Stochastic domination in predictive density estimation for ordered normal means under \(\alpha\)-divergence loss ⋮ Universal inadmissibility of least squares estimator ⋮ Comparison of normal variance estimators under multiple criteria and towards a compromise estimator ⋮ Some applications of Anderson's inequality to some optimality criteria of the generalized least squares estimator ⋮ Unnamed Item ⋮ Estimation of two order restricted normal means with unknown and possibly unequal variances ⋮ Optimal substitution of arguments of an arrangement increasing function based on sufficient statistics for parameter-arguments ⋮ On a class of improved estimators of variance and estimation under order restriction ⋮ A note on stochastic domination for discrete exponential families ⋮ Shrinkage estimation of a correlation coefficient and two examples with real life data-sets ⋮ Stochastic ordering and Schur-convex functions in comparison of linear experiments ⋮ Estimating the center of symmetry: is it always better to use larger sample sizes? ⋮ Pitman's measure of closeness for symmetric stable distributions ⋮ Shape optimal design criterion in linear models ⋮ On the characterization of Pitman measure of nearness ⋮ Robust estimation of location in samples of size three ⋮ Improved confidence sets of spherically symmetric distributions ⋮ Stein estimation -- a review ⋮ Comparison of estimators of the location parameter using pitman's closeness criterion ⋮ some properties of posterior pitman closeness ⋮ Pitman closeness for hierarchical bayes predictors in mixed linear models ⋮ Universal Domination and Stochastic Domination—an Improved lower Bound for the Dimensionality ⋮ A maximal extension of the Gauss-Markov theorem and its nonlinear version. ⋮ Confidence interval estimation under some restrictions on the parameters with nonlinear boundaries
This page was built for publication: Universal domination and stochastic domination: Estimation simultaneously under a broad class of loss functions
