Results 141 to 150 of about 4,352 (182)

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

Shannon optimal priors on independent identically distributed statistical experiments converge weakly to Jeffreys' prior

Test, 1998
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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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Information in Jeffreys Prior

2022
Jeffreys prior is well-known to give invariance of probability of prior distributions under transformation, but this invariance does not mean non-informativeness, is pointed out by Okamoto (2010) (Okamoto2010.pdf in the Files box below). Okamoto (2010) discusses on the case of item response theory (IRT).
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Jeffreys priors for survival models with censored data

Journal of Statistical Planning and Inference, 2001
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DE SANTIS, Fulvio, Julia Mortera, Alessandra Nardi   +2 more
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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
openaire   +3 more sources

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
openaire   +1 more source

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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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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