Displaying similar documents to “On the Jensen-Shannon divergence and the variation distance for categorical probability distributions”

Information-type divergence when the likelihood ratios are bounded

Andrew Rukhin (1997)

Applicationes Mathematicae

Similarity:

The so-called ϕ-divergence is an important characteristic describing "dissimilarity" of two probability distributions. Many traditional measures of separation used in mathematical statistics and information theory, some of which are mentioned in the note, correspond to particular choices of this divergence. An upper bound on a ϕ-divergence between two probability distributions is derived when the likelihood ratio is bounded. The usefulness of this sharp bound is illustrated by several...

Likelihood and the Bayes procedure.

Hirotugu Akaike (1980)

Trabajos de Estadística e Investigación Operativa

Similarity:

In this paper the likelihood function is considered to be the primary source of the objectivity of a Bayesian method. The necessity of using the expected behaviour of the likelihood function for the choice of the prior distribution is emphasized. Numerical examples, including seasonal adjustment of time series, are given to illustrate the practical utility of the common-sense approach to Bayesian statistics proposed in this paper.

Asymptotic theory: some recent developments.

David R. Cox (1983)

Qüestiió

Similarity:

A review is given of recent work on asymptotic theory leading to a recommendation to use ratio likelihood rests with, where available, a Bartlett adjustment factor.

On maximum entropy priors and a most likely likelihood in auditing.

Agustín Hernández Bastida, María del C. Martel Escobar, Francisco José Vázquez Polo (1998)

Qüestiió

Similarity:

There are two basic questions auditors and accountants must consider when developing test and estimation applications using Bayes' Theorem: What prior probability function should be used and what likelihood function should be used. In this paper we propose to use a maximum entropy prior probability function MEP with the most likely likelihood function MLL in the Quasi-Bayesian QB model introduced by McCray (1984). It is defined on an adequate parameter. Thus procedure only needs an expected...

Approximate Bayesian methods.

Dennis V. Lindley (1980)

Trabajos de Estadística e Investigación Operativa

Similarity:

This paper develops asymptotic expensions for the ratios of integrals that occur in Bayesian analysis: for example, the posterior mean. The first term omitted is 0(n) and it is shown how the term 0(n) can be of importance.

Empirical analysis of current status data for additive hazards model with auxiliary covariates

Jianling Zhang, Mei Yang, Xiuqing Zhou (2021)

Kybernetika

Similarity:

In practice, it often occurs that some covariates of interest are not measured because of various reasons, but there may exist some auxiliary information available. In this case, an issue of interest is how to make use of the available auxiliary information for statistical analysis. This paper discusses statistical inference problems in the context of current status data arising from an additive hazards model with auxiliary covariates. An empirical log-likelihood ratio statistic for...

Likelihood for random-effect models (with discussion).

Youngjo Lee, John A. Nelder (2005)

SORT

Similarity:

For inferences from random-effect models Lee and Nelder (1996) proposed to use hierarchical likelihood (h-likelihood). It allows influence from models that may include both fixed and random parameters. Because of the presence of unobserved random variables h-likelihood is not a likelihood in the Fisherian sense. The Fisher likelihood framework has advantages such as generality of application, statistical and computational efficiency. We introduce an extended likelihood framework and...

Some history of the hierarchical Bayesian methodology.

Irving John Good (1980)

Trabajos de Estadística e Investigación Operativa

Similarity:

A standard tecnique in subjective Bayesian methodology is for a subject (you) to make judgements of the probabilities that a physical probability lies in various intervals. In the Bayesian hierarchical technique you make probability judgements (of a higher type, order, level or stage) concerning the judgements of lower type. The paper will outline some of the history of this hierarchical technique with emphasis on the contributions by I. J. Good because I have read every word written...