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Showing papers in "Journal of the royal statistical society series b-methodological in 1997"

Journal Article•
On Bayesian analysis of mixtures with an unknown number of components. Discussion. Author's reply

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S. Richardson, Peter H.R. Green, Christian P. Robert, M. Aitkin, David Cox, M. Stephens, A. Polymenis, Walter R. Gilks, A. Nobile, M. Hodgson, Anthony O'Hagan, N. T. Longford, A. P. Dawid, Anthony C. Atkinson, José M. Bernardo, Julian Besag, Stephen Brooks, S. Byers, Adrian E. Raftery, Gilles Celeux, R. C. H. Cheng, W. B. Liu, Y.-H. Chien, Edward I. George, Noel A Cressie, H.-C. Huang, M.-A. Gruet, S. C. Heath, Christopher Jennison, Andrew B. Lawson, A. Clark, Geoffrey J. McLachlan, D. Peel, Kerrie Mengersen, A. George, A. Philippe, K. Roeder, Larry Wasserman, P. Schlattmann, D. Böhning, D. M. Titterington, H. Tong, Mike West 
01 Jan 1997-Journal of the royal statistical society series b-methodological
TL;DR: In this paper, a hierarchical prior model is proposed to deal with weak prior information while avoiding the mathematical pitfalls of using improper priors in the mixture context, and a sample from the full joint distribution of all unknown variables is generated, which can be used as a basis for a thorough presentation of many aspects of the posterior distribution.
Abstract: New methodology for fully Bayesian mixture analysis is developed, making use of reversible jump Markov chain Monte Carlo methods that are capable of jumping between the parameter subspaces corresponding to different numbers of components in the mixture. A sample from the full joint distribution of all unknown variables is thereby generated, and this can be used as a basis for a thorough presentation of many aspects of the posterior distribution. The methodology is applied here to the analysis of univariate normal mixtures, using a hierarchical prior model that offers an approach to dealing with weak prior information while avoiding the mathematical pitfalls of using improper priors in the mixture context.

258 citations

Journal Article•
Estimating the Fractal Dimension of a Locally Self-similar Gaussian Process by using Increments

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John T. Kent1, Andrew T. A. WOODt1•
University of Bath1
01 Jan 1997-Journal of the royal statistical society series b-methodological
TL;DR: In this article, the problem of estimating the parameter ca of a stationary Gaussian process with respect to a fixed parameter is considered, and the problem is solved by estimating the parameters of a Gaussian Process with
Abstract: SUMMARY Consider the problem of estimating the parameter ca of a stationary Gaussian process with

147 citations

Journal Article•
Discussion to 'Predicting multivariate responses in multiple linear regression' by L. Breiman & J.H. Friedman

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C.J.F. ter Braak
01 Jan 1997-Journal of the royal statistical society series b-methodological

25 citations

Journal Article•
Discussion of Copas, J.B. and LI, H.G.: inference for non-random samples

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Geert Molenberghs
01 Jan 1997-Journal of the royal statistical society series b-methodological

15 citations

Journal Article•
Fractal function estimation via wavelet shrinkage

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I. Wang
01 Jan 1997-Journal of the royal statistical society series b-methodological
TL;DR: In this paper, it was shown that wavelet shrinkage can estimate fractal functions with their fractal dimensions virtually preserved, and the authors used this technique to estimate the roughness of objects.
Abstract: In scientific studies objects are often very rough. Mathematically these rough objects are modelled by fractal functions, and the fractal dimension is usually used to measure their roughness. This paper investigates fractal function estimation by wavelet shrinkage. It is shown that wavelet shrinkage can estimate fractal functions with their fractal dimensions virtually preserved.

1 citations