Improved nonparametric estimation of location vectors in multivariate regression models

DOI10.1080/10485259908832775zbMath0954.62046OpenAlexW2087689958MaRDI QIDQ3836390

A. K. Md. Ehsanes Saleh, S. Ejaz Ahmed

Publication date: 30 January 2001

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10485259908832775

zbMATH Keywords

asymptotic biasasymptotic distributional riskshrinkage estimatorsnonparametric multivariate estimationrank order estimators

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic properties of nonparametric inference (62G20)

Related Items (9)

Constrained estimation and some useful results in several multivariate models ⋮ Performances of the Positive-Rule Stein-Type Ridge Estimator in a Linear Regression Model with Spherically Symmetric Error Distributions ⋮ Preliminary test and estimation in some multifactor diffusion processes ⋮ Shrinkage strategy in stratified random sample subject to measurement error ⋮ A simple formula for asymptotic distributional risk of some estimators ⋮ Shrinkage estimation in general linear models ⋮ Assessing the process capability index for non-normal processes ⋮ Nonparametric inference strategies for the quantile functions under left truncation and right censoring ⋮ Stein-rule estimation under an extended balanced loss function

Cites Work

• Nonparametric estimation of location parameter after a preliminary test on regression
• Nonparametric estimation of location parameter after a preliminary test on regression in the multivariate case
• Estimation strategies for the intercept vector in a simple linear multivariate normal regression model
• On shrinkage least squares estimation in a parallelism problem
• Inference based on conditional speclfication
• On shrinkage m-estimators of location parameters
• Asymptotic Linearity of a Rank Statistic in Regression Parameter
• On Biases in Estimation Due to the Use of Preliminary Tests of Significance

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