An adaptive weighted least squares ratio approach for estimation of heteroscedastic linear regression model in the presence of outliers

DOI10.1080/03610918.2021.1907408MaRDI QIDQ6172597

Muhammad Aslam, Zahra Zafar

Publication date: 20 July 2023

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

zbMATH Keywords

outliersordinary least squares estimatorheteroscedastic errorsadaptive weighted least squaresadaptive weighted least squares ratio estimatorleast squares ratio estimator

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Asymptotic inference under heteroskedasticity of unknown form
• Adapting for heteroscedasticity in linear models
• Robust estimation in heteroscedastic linear models
• Estimation for a linear regression model with unknown diagonal covariance matrix
• Inference under Heteroscedasticity of Unknown Form Using an Adaptive Estimator
• Model selection using information criteria under a new estimation method: least squares ratio
• An outlier-resistant test for heteroscedasticity in linear models
• Classification trees aided mixed regression model
• A New Robust Regression Method Based on Particle Swarm Optimization
• Efficient Estimation and Robust Inference of Linear Regression Models in the Presence of Heteroscedastic Errors and High Leverage Points
• A Novel Regression Approach: Least Squares Ratio

This page was built for publication: An adaptive weighted least squares ratio approach for estimation of heteroscedastic linear regression model in the presence of outliers