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On Divergence Measures Leading to Jeffreys and Other Reference Priors

Bayesian Analysis, 2014
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Malay Ghosh, Jayanta K Ghosh
exaly   +3 more sources

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On the Jeffreys Prior for the Multivariate Ewens Distribution

Statistics and Probability Letters, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Abel Rodriguez
exaly   +2 more sources

Jeffreys prior regularization for logistic regression

2016 IEEE Statistical Signal Processing Workshop (SSP), 2016
Logistic regression is a statistical model widely used for solving classification problems. Maximum likelihood is used train the model parameters. When data from two classes is linearly separable, maximum likelihood is ill-posed. To address this problem as well as to handle over-fitting issues, regularization is commonly considered.
Tam Nguyen, Raviv Raich, Phung Lai
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A Comparison of Bayes–Laplace, Jeffreys, and Other Priors

The American Statistician, 2008
Beta distributions with both parameters equal to 0, ½, or 1 are the usual choices for “noninformative” priors for Bayesian estimation of the binomial parameter. However, as illustrated by two examples from the Bayesian literature, care needs to be taken with parameter values below 1, both for noninformative and informative priors, as such priors ...
Tuyl, Frank, Gerlach, Richard, Mengersen, Kerrie   +2 more
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Jeffreys' prior yields the asymptotic minimax redundancy

Proceedings of 1994 Workshop on Information Theory and Statistics, 2002
We determine the asymptotic minimax redundancy of universal data compression in a parametric setting and show that it corresponds to the use of Jeffreys prior. Statistically, this formulation of the coding problem can be interpreted in a prior selection context and in an estimation context.
B.S. Clarke, A.R. Barron
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Jeffreys prior for mixture models [PDF]

, 2014
Mixture models may be a useful and flexible tool to describe data with a complicated structure, for instance characterized by multimodality or asymmetry. In a Bayesian setting, it is a well established fact that one need to be careful in using improper prior distributions, since the posterior distribution may not be proper.
GRAZIAN, CLARA, C. P. Robert
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Bayesian Inference for Nested Designs Based on Jeffreys's Prior

American Statistician, 1997
Abstract Bayesian intervals for linear combinations of the means in a balanced nested design based on Jeffreys's prior are found to be conservative in the sense that the p% Bayesian interval derived from Jeffreys's prior always contains the p% confidence interval derived from the ANOVA test.
Hal S. Stern
exaly   +2 more sources

Jeffreys' prior for logit models

Journal of Econometrics, 1994
Abstract This paper investigates Jeffreys' prior for logit models with covariates. In conditional logit models it is not recommended, and in multinomial logit models it has properties similar to a neutral natural conjugate prior, but none of the computational advantages.
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Jeffreys Prior for Negative Binomial and Zero Inflated Negative Binomial Distributions

Sankhya A, 2022
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Arnab Kumar Maity, Erina Paul
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Bias reduction, the Jeffreys prior and GLIM

1992
In full exponential-family models, such as generalized linear models with canonical link, use of the Jeffreys invariant prior as a penalty function removes the leading term from the asymptotic bias of maximum likelihood estimates. In Poisson log linear and binomial logistic linear models, the posterior mode can be calculated using the standard ...
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