Displaying similar documents to “Model selection with vague prior information”

On the frequentist and Bayesian approaches to hypothesis testing.

Elías Moreno, F. Javier Girón (2006)

SORT

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Hypothesis testing is a model selection problem for which the solution proposed by the two main statistical streams of thought, frequentists and Bayesians, substantially differ. One may think that this fact might be due to the prior chosen in the Bayesian analysis and that a convenient prior selection may reconcile both approaches. However, the Bayesian robustness viewpoint has shown that, in general, this is not so and hence a profound disagreement between both approaches exists. In...

Bayesian inference in applied statistics.

Arthur P. Dempster (1980)

Trabajos de Estadística e Investigación Operativa

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The task of assessing posterior distributions from noisy empirical data imposes difficult requirements of modelling, computing and assessing sensitivity to model choice. Seasonal analysis of economic time series is used to illustrate ways of approaching such difficulties.

Sensitivity to models: Discussion.

William F. Eddy, Anthony O'Hagan, José M. Bernardo, Philip J. Brown, A. Philip Dawid, James M. Dickey, Irving John Good, Adrian F. M. Smith (1980)

Trabajos de Estadística e Investigación Operativa

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Discussion on the papers by Freeman, Peter R., On the number of outliers in data from a linear model and by Box, George E. P., Sampling inference, Bayes' inference and robustness in the advancement of learning, both of them part of a round table on Sensitivity to models held in the First International Congress on Bayesian Methods (Valencia, Spain, 28 May - 2 June 1979).

Sequential learning, discontinuities and changes: Discussion.

Stephen E. Fienberg, José M. Bernardo, Philip J. Brown, A. Philip Dawid, James M. Dickey, Joseph B. Kadane, Tom Leonard (1980)

Trabajos de Estadística e Investigación Operativa

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Discussion on the papers by Makov, Udi E., Approximation of unsupervised Bayes learning procedures, Smith, Adrian F. M., Change-Point problems: approaches and applications and by Harrison, P. J. and Smith Jim Q., Discontinuity, decision and conflict, the three of them part of a round table on Sequential learning, discontinuities and changes held in the First International Congress on Bayesian Methods (Valencia, Spain, 28 May - 2 June 1979).

Hypothesis testing: Discussion.

Edwin T. Jaynes, David J. Spiegelhalter, Hirotugu Akaike, Arthur P. Dempster, James M. Dickey, Seymour Geisser, Irving John Good, Dennis V. Lindley, Anthony O'Hagan, Arnold Zellner (1980)

Trabajos de Estadística e Investigación Operativa

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Discussion on the papers by Zellner, Arnold and Siow, Aloysius, Posterior odds ratios for selected regression hypotheses and by Bernardo, José M., A Bayesian analysis of classical hypotheses testing, both of them part of a round table on Hypothesis testing held in the First International Congress on Bayesian Methods (Valencia, Spain, 28 May - 2 June 1979).

Intrinsic priors for hypothesis testing in normal regression models.

Elías Moreno, F. Javier Girón, Francisco Torres (2003)

RACSAM

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Testing that some regression coefficients are equal to zero is an important problem in many applications. Homoscedasticity is not necessarily a realistic condition in this setting and, as a consequence, no frequentist test there exist. Approximate tests have been proposed. In this paper a Bayesian analysis of this problem is carried out, from a default Bayesian model choice perspective. Explicit expressions for intrinsic priors are provided, and it is shown that the corresponding Bayes...

On the number of outliers in data from a linear model.

Peter R. Freeman (1980)

Trabajos de Estadística e Investigación Operativa

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This paper reviews models for the occurrence of outliers in data from the linear model. The Bayesian analyses are all closely similar in form, but differ in the way they treat suspected outliers. The models are compared on Darwin's data and one of them is used on data from a 25 factorial experiment. The question on how many outliers are present involves comparison of models with different number of parameters. A solution using proper priors on all parameters...

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.