Displaying similar documents to “Three methods for constructing reference prior distributions.”

A Bayesian look at nuisance parameters.

A. Philip Dawid (1980)

Trabajos de Estadística e Investigación Operativa

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The elimination of nuisance parameters has classically been tackled by various ad hoc devices, and has led to a number of attemps to define partial sufficiency and ancillarity. The Bayesian approach is clearly defined. This paper examines some classical procedures in order to see when they can be given a Bayesian justification.

Predictive sample reuse: Discussion.

Irwin Guttman, S. James Press (1980)

Trabajos de Estadística e Investigación Operativa

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Discussion on the paper by Geisser, Seymour, Predictive sample reuse techniques for censored data, part of a round table on Bayesian and non-Bayesian conditional inference held in the First International Congress on Bayesian Methods (Valencia, Spain, 28 May - 2 June 1979).

Pivotal inference and the Bayesian controversy.

George A. Barnard (1980)

Trabajos de Estadística e Investigación Operativa

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The theory of pivotal inference applies when parameters are defined by reference to their effect on observations rather than their effect on distributions. It is shown that pivotal inference embraces both Bayesian and frequentist reasoning.

Improving predictive distributions.

Morris H. DeGroot (1980)

Trabajos de Estadística e Investigación Operativa

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Consider a sequence of decision problems S, S, ... and suppose that in problem S the statistician must specify his predictive distribution F for some random variable X and make a decision based on that distribution. For example, X might be the return on some particular investment and the statistician must decide whether or not to make that investment. The random variables X, X, ... are assumed to be independent and completely unrelated. It is also assumed that each predictive distribution...

Approximate Bayesian methods.

Dennis V. Lindley (1980)

Trabajos de Estadística e Investigación Operativa

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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.

Unimodal contaminations in testing point null hypothesis.

Miguel Angel Gómez-Villegas, Luís Sanz (2003)

RACSAM

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The problem of testing a point null hypothesis from the Bayesian perspective is considered. The uncertainties are modelled through use of ε?contamination class with the class of contaminations including: i) All unimodal distributions and ii) All unimodal and symmetric distributions. Over these classes, the infimum of the posterior probability of the point null hypothesis is computed and compared with the p?value and a better approach than the one known is obtained.

Predictive sample reuse techniques for censored data.

Seymour Geisser (1980)

Trabajos de Estadística e Investigación Operativa

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Predictive sample reuse methods usually applied in low structure aparametric paradigms are shown to be useful in certain high structure situations when conjoined with a Bayesian approach. Particular attention is focused on the incomplete data situation for which two alternative sample reuse approaches are devised. The first involves differential weighting and the second a recursive sample reuse algorithm. These are applied to censored exponential survival data. The exponential approach...

Univariate parametric survival analysis using GS-distributions.

Albert Sorribas, José M. Muiño, Montserrat Rué, Joan Fibla (2006)

SORT

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The GS-distribution is a family of distributions that provide an accurate representation of any unimodal univariate continuous distribution. In this contribution we explore the utility of this family as a general model in survival analysis. We show that the survival function based on the GS-distribution is able to provide a model for univariate survival data and that appropriate estimates can be obtained. We develop some hypotheses tests that can be used for checking the underlying survival...