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Specifying Statistical Models

From Parametric to Non-Parametric, Using Bayesian or Non-Bayesian Approaches

  • Conference proceedings
  • © 1983

Overview

Editors:
  1. J. P. Florens

    1. Université d’Aix-Marseille II, France

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  2. M. Mouchart

    1. C.O.R.E., Université Catholique de Louvain, Belgium

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  3. J. P. Raoult

    1. Université de Rouen, France

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  4. L. Simar

    1. Facultés Universitaires Saint-Louis, Bruxelles, Belgium

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  5. A. F. M. Smith

    1. University of Nottingham, UK

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Part of the book series: Lecture Notes in Statistics (LNS, volume 16)

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Table of contents (12 papers)

  1. Front Matter

    Pages i-xii
    Download chapter PDF

  2. Protecting Against Gross Errors: The Aid of Bayesian Methods

    • Léopold Simar
    Pages 1-12

  3. Bayesian Approaches to Outliers and Robustness

    • A. F. M. Smith
    Pages 13-35

  4. The Probability Integral Transformation for Non Necessarily Absolutely Continuous Distribution Functions, and its Application to Goodness-of-Fit Tests

    • J. P. Raoult, D. Criticou, D. Terzakis
    Pages 36-49

  5. Simulation in the General First Order Autoregressive Process (Unidimensional Normal Case)

    • Paul Doukhan
    Pages 50-68

  6. Non Parametric Prediction in Stationary Processes

    • Denis Bosq
    Pages 69-84

  7. Approximate Reductions of Bayesian Experiments

    • J. P. Florens
    Pages 85-92

  8. Theory and Applications of Least Squares Approximation in Bayesian Analysis

    • M. Mouchart, L. Simar
    Pages 93-107

  9. Non Parametric Bayesian Statistics: A Stochastic Process Approach

    • J. M. Rolin
    Pages 108-133

  10. Robust Testing for Independent Non Identically Distributed Variables and Markov Chains

    • Lucien Birgé
    Pages 134-162

  11. “On the Use of Some Variation Distance Inequalities to Estimate the Difference between Sample and Perturbed Sample”

    • A. Hillion
    Pages 163-175

  12. A Contribution to Robust Principal Component Analysis

    • Jacques Benasseni
    Pages 176-181

  13. From Non Parametric Regression to Non Parametric Prediction: Survey of the Mean Square Error and Original Results on the Predictogram

    • Gérard Collomb
    Pages 182-204
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Keywords

About this book

During the last decades. the evolution of theoretical statistics has been marked by a considerable expansion of the number of mathematically and computationaly trac­ table models. Faced with this inflation. applied statisticians feel more and more un­ comfortable: they are often hesitant about their traditional (typically parametric) assumptions. such as normal and i. i. d . • ARMA forms for time-series. etc . • but are at the same time afraid of venturing into the jungle of less familiar models. The prob­ lem of the justification for taking up one model rather than another one is thus a crucial one. and can take different forms. (a) ~~~£ifi~~~iQ~ : Do observations suggest the use of a different model from the one initially proposed (e. g. one which takes account of outliers). or do they render plau­ sible a choice from among different proposed models (e. g. fixing or not the value of a certai n parameter) ? (b) tlQ~~L~~l!rQ1!iIMHQ~ : How is it possible to compute a "distance" between a given model and a less (or more) sophisticated one. and what is the technical meaning of such a "distance" ? (c) BQe~~~~~~ : To what extent do the qualities of a procedure. well adapted to a "small" model. deteriorate when this model is replaced by a more general one? This question can be considered not only. as usual. in a parametric framework (contamina­ tion) or in the extension from parametriC to non parametric models but also.

Editors and Affiliations

  • Université d’Aix-Marseille II, France

    J. P. Florens

  • C.O.R.E., Université Catholique de Louvain, Belgium

    M. Mouchart

  • Université de Rouen, France

    J. P. Raoult

  • Facultés Universitaires Saint-Louis, Bruxelles, Belgium

    L. Simar

  • University of Nottingham, UK

    A. F. M. Smith

Bibliographic Information

  • Book Title: Specifying Statistical Models

  • Book Subtitle: From Parametric to Non-Parametric, Using Bayesian or Non-Bayesian Approaches

  • Editors: J. P. Florens, M. Mouchart, J. P. Raoult, L. Simar, A. F. M. Smith

  • Series Title: Lecture Notes in Statistics

  • DOI: https://doi.org/10.1007/978-1-4612-5503-1

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media New York 1983

  • Softcover ISBN: 978-0-387-90809-0Published: 24 January 1983

  • eBook ISBN: 978-1-4612-5503-1Published: 06 December 2012

  • Series ISSN: 0930-0325

  • Series E-ISSN: 2197-7186

  • Edition Number: 1

  • Number of Pages: XII, 204

  • Topics: Applications of Mathematics

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