Specifying Statistical Models -

Specifying Statistical Models

From Parametric to Non-Parametric, Using Bayesian or Non-Bayesian Approaches
Buch | Softcover
204 Seiten
1983 | Softcover reprint of the original 1st ed. 1983
Springer-Verlag New York Inc.
978-0-387-90809-0 (ISBN)
53,49 inkl. MwSt
(a) ~~~£ifi~~~iQ~ : Do observations suggest the use of a different model from the one initially proposed (e. in a parametric framework (contamina­ tion) or in the extension from parametriC to non parametric models but also.
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.

1. Protecting Against Gross Errors: The Aid of Bayesian Methods.- 2. Bayesian Approaches to Outliers and Robustness.- 3. The Probability Integral Tranformation for Non-Necessary Absolutely Continuous Distribution Functions, and its Application to Goodness-of-Fit Tests.- 4. Simulation in the General First Order Autoregressive Process (Unidimensional Normal Case).- 5. Non Parametric Prediction in Stationary Processes.- 6. Approximate Reductions of Bayesian Experiments.- 7. Theory and Applications of Least Squares Approximation in Bayesian Analysis.- 8. Non Parametric Bayesian Statistics: A Stochastic Process Approach.- 9. Robust Testing for Independent Non-Identically Distributed Variables and Markov Chains.- 10. On the Use of some Variation Distance Inequalities to estimate the Difference between Sample and Perturbed Sample.- 11. A Contribution to Robust Principal Component Analysis.- 12. From Non Parametric Regression to Non Parametric Prediction: Survey of the Mean Square Error and Original Results on the Predictogram.

Reihe/Serie Lecture Notes in Statistics ; 16
Zusatzinfo XII, 204 p.
Verlagsort New York, NY
Sprache englisch
Maße 155 x 235 mm
Themenwelt Mathematik / Informatik Mathematik Algebra
Mathematik / Informatik Mathematik Angewandte Mathematik
Mathematik / Informatik Mathematik Statistik
ISBN-10 0-387-90809-9 / 0387908099
ISBN-13 978-0-387-90809-0 / 9780387908090
Zustand Neuware
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Buch | Softcover (2022)
Springer Spektrum (Verlag)
54,99