Robust estimation and variable selection in heteroscedastic regression model using least favorable distribution
type: Item
instance of: scholarly article
title: Robust estimation and variable selection in heteroscedastic regression model using least favorable distribution
zbMATH Open document ID: 1505.62169
author: Yeşim Güney
author: Yetkin Tuaç
author: Şenay Özdemir
author: Olcay Arslan
published in: Computational Statistics
publication date: 2021-06-16T00:00:00Z
Mathematics Subject Classification ID: 62-08
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62J07
Mathematics Subject Classification ID: 62F12
Mathematics Subject Classification ID: 62F35
zbMATH DE Number: 7360095
zbMATH Keywords: least favorable distribution
zbMATH Keywords: joint location and scale model
zbMATH Keywords: robust parameter estimation
zbMATH Keywords: robust variable selection
describes a project that uses: GLIM
describes a project that uses: R
MaRDI profile type: Publication
full work available at URL: s00180-020-01036-5
OpenAlex ID: W3094720465
cites work: Joint estimation and variable selection for mean and dispersion in proper dispersion models
cites work: Weighted LAD-LASSO method for robust parameter estimation and variable selection in regression
cites work: Penalized MM regression estimation withLγpenalty: a robust version of bridge regression
cites work: A Simple Test for Heteroscedasticity and Random Coefficient Variation
cites work: LASSO-TYPE GMM ESTIMATOR
cites work: Q4101268
cites work: Least angle regression. (With discussion)
cites work: Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
cites work: A Statistical View of Some Chemometrics Regression Tools
cites work: Q3723487
cites work: Estimating Regression Models with Multiplicative Heteroscedasticity
cites work: Robust Estimation of a Location Parameter
cites work: Robust Statistics
cites work: Asymptotics for Lasso-type estimators.
cites work: Q3074777
cites work: A skew-normal mixture of joint location, scale and skewness models
cites work: Variable selection in joint location, scale and skewness models of the skew-normal distribution
cites work: Q5447530
cites work: Joint modelling of location and scale parameters of the t distribution
cites work: Q4864293
cites work: Q4272815
cites work: Weighted Wilcoxon‐Type Smoothly Clipped Absolute Deviation Method
cites work: Robust Variable Selection With Exponential Squared Loss
cites work: Variable Selection in Joint Location and Scale Models of the Skew-t-Normal Distribution
cites work: Variable selection for joint mean and dispersion models of the inverse Gaussian distribution
cites work: Variable selection in joint mean and variance models of Box–Cox transformation
cites work: Variable selection in joint location and scale models of the skew-normal distribution
cites work: Variable selection in joint location, scale and skewness models with a skew-\(t\)-normal distribution
cites work: Adaptive penalized quantile regression for high dimensional data
cites work: Globally adaptive quantile regression with ultra-high dimensional data
cites work: Robust adaptive Lasso for variable selection
DOI: 10.1007/S00180-020-01036-5
description: scientific article; zbMATH DE number 7360095