Robust and efficient estimation in ordinal response models using the density power divergence

DOI10.1080/02331888.2024.2347329MaRDI QIDQ6618187

Author name not available (Why is that?), Ayanendranath Basu, Subhrajyoty Roy, Abhik Ghosh

Publication date: 14 October 2024

Published in: Statistics (Search for Journal in Brave)

Mathematics Subject Classification ID

Robustness and adaptive procedures (parametric inference) (62F35)

Cites Work

• Title not available (Why is that?)
• Robust estimation for independent non-homogeneous observations using density power divergence with applications to linear regression
• Robust estimation for ordinal regression
• Breakdown points of trimmed likelihood estimators and related estimators in generalized linear models.
• Robust fitting of the binomial model.
• The breakdown behavior of the maximum likelihood estimator in the logistic regression model.
• A generalized framework for modelling ordinal data
• Robustness issues for \textsc{cub} models
• Robust inference for ordinal response models
• Robust estimation in the normal mixture model
• Minimum disparity estimation: asymptotic normality and breakdown point results
• Robust and efficient estimation by minimising a density power divergence
• Robust Estimation for Grouped Data
• Robust link functions
• Robust estimation for non-homogeneous data and the selection of the optimal tuning parameter: the density power divergence approach
• Choosing a robustness tuning parameter
• Bounded-Influence Robust Estimation in Generalized Linear Latent Variable Models
• On the ‘optimal’ density power divergence tuning parameter

This page was built for publication: Robust and efficient estimation in ordinal response models using the density power divergence

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6618187)