Conjugate priors and bias reduction for logistic regression models
From MaRDI portal
Publication:6178693
DOI10.1016/J.SPL.2023.109901arXiv2202.08734MaRDI QIDQ6178693
Tommaso Rigon, Emanuele Aliverti
Publication date: 4 September 2023
Published in: Statistics \& Probability Letters (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2202.08734
Point estimation (62F10) Bayesian inference (62F15) Generalized linear models (logistic models) (62J12) Characterization and structure theory of statistical distributions (62E10)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Mean and median bias reduction in generalized linear models
- Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
- Conjugate priors for exponential families
- Intrinsic losses
- A generic algorithm for reducing bias in parametric estimation
- Conditionally conjugate mean-field variational Bayes for logistic models
- Generalized Conjugate Priors for Bayesian Analysis of Risk and Survival Regressions
- 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
- Bias reduction of maximum likelihood estimates
- A modern maximum-likelihood theory for high-dimensional logistic regression
- Median bias reduction of maximum likelihood estimates
- Jeffreys-prior penalty, finiteness and shrinkage in binomial-response generalized linear models
This page was built for publication: Conjugate priors and bias reduction for logistic regression models