Stochastic analysis of covariance when the error distribution is long-tailed symmetric

DOI10.1080/02664763.2015.1125866OpenAlexW2284035162MaRDI QIDQ5138137

Olcay Arslan, Pelin Kasap, Birdal Şenoğlu

Publication date: 3 December 2020

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

Full work available at URL: https://doi.org/10.1080/02664763.2015.1125866

zbMATH Keywords

robustnessANCOVAstochastic covariatelong-tailed symmetriciteratively reweighting algorithm

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Estimation in bivariate nonnormal distributions with stochastic variance functions
• Dynamic system identification. Experiment design and data analysis
• Convergence behavior of an iterative reweighting algorithm to compute multivariate M-estimates for location and scatter
• Estimation and hypothesis testing for a new family of bivariate nonnormal distributions
• Multiple Linear Regression Model Under Nonnormality
• Modified maximum likelihood estimators for the bivariate normal based on type ii censored samples
• Robust 2^kfactorial design with Weibull error distributions
• Robust Estimation and Hypothesis Testing of Linear Contrasts in Analysis of Covariance with Stochastic Covariates
• Robust Analysis of Covariance
• The asymptotic properties of ML estimators when sampling from associated populations
• Estimating parameters in autoregressive models in non-normal situations: symmetric innovations
• NONNORMAL REGRESSION. II. SYMMETRIC DISTRIBUTIONS
• ANALYSIS OF VARIANCE IN EXPERIMENTAL DESIGN WITH NONNORMAL ERROR DISTRIBUTIONS
• Regression Analysis with a Stochastic Design Variable
• Estimation and hypothesis testing for a nonnormal bivariate distribution with applications
• Weibull distributions when the shape parameter is defined.
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Stochastic analysis of covariance when the error distribution is long-tailed symmetric