Improved Estimation in Cumulative Link Models

DOI10.1111/rssb.12025zbMath1411.62217arXiv1204.0105OpenAlexW2059609530MaRDI QIDQ5743267

Ioannis Kosmidis

Publication date: 9 May 2019

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1204.0105

zbMATH Keywords

shrinkageordinal response modelsadjusted score equationsreduction of biasadjusted counts

Mathematics Subject Classification ID

Generalized linear models (logistic models) (62J12) Contingency tables (62H17) Continuous-time Markov processes on discrete state spaces (60J27)

Related Items

Tree-based varying coefficient regression for longitudinal ordinal responses, Location-adjusted Wald statistics for scalar parameters, On the Hauck–Donner Effect in Wald Tests: Detection, Tipping Points, and Parameter Space Characterization, Median bias reduction in cumulative link models, Improved estimators in beta prime regression models, Semi-parametric bivariate polychotomous ordinal regression, Improved estimation in a general multivariate elliptical model, Mean and median bias reduction in generalized linear models

Uses Software

• R
• pscl
• ordinal

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Bias reduction in exponential family nonlinear models
• Defining the curvature of a statistical problem (with applications to second order efficiency)
• Inversion of Jacobi's tridiagonal matrix
• A modified score function estimator for multinomial logistic regression in small samples.
• A generic algorithm for reducing bias in parametric estimation
• Multinomial logit bias reduction via the Poisson log-linear model
• THE ESTIMATION AND SIGNIFICANCE OF THE LOGARITHM OF A RATIO OF FREQUENCIES
• On estimating binomial response relations
• On the existence of maximum likelihood estimates in logistic regression models
• Partial Proportional Odds Models for Ordinal Response Variables
• Concavity of the Log Likelihood
• Likelihood-Based Methods for Bias Reduction in Limiting Dilution Assays
• Bias reduction of maximum likelihood estimates
• Ridge Estimators in Logistic Regression
• On the bias of various estimators of the logit and its variance with application to quantal bioassay