Nonparametric Monte Carlo Tests and Their Applications
A fundamental issue in statistical analysis is testing the fit of a particular probability model to a set of observed data. Monte Carlo approximation to the null distribution of the test provides a convenient and powerful means of testing model fit. Nonparametric Monte Carlo Tests and Their Applicat...
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| Autor principal: | |
|---|---|
| Format: | Livre numérique |
| Idioma: | Anglais |
| Publicat: |
New York, NY :
Springer New York
[20..].
Cham : Springer Nature |
| Edició: | 1st ed. 2005. |
| Col·lecció: | Lecture Notes in Statistics
182 |
| Accés en línia: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Nonparametric Monte Carlo tests and their applications, Lixing Zhu, 2005, New York, Springer, 1 vol. (XI-181 p.), Lecture notes in statistics, 0-387-25038-7 • Nonparametric Monte Carlo Tests and Their Applications, Texte imprimé, 9780387505640 • Nonparametric Monte Carlo tests and their applications, Lixing Zhu, 2005, New York, Springer, 1 vol. (XI-181 p.), Lecture notes in statistics, 0-387-25038-7 |
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| 100 | 1 | |a Zhu, Lixing. | |
| 245 | 1 | 0 | |a Nonparametric Monte Carlo Tests and Their Applications |c by Lixing Zhu. |
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| 505 | 1 | |a Monte Carlo Tests Testing for Multivariate Distributions Asymptotics of Goodness-of-fit Tests for Symmetry A Test of Dimension-Reduction Type for Regressions Checking the Adequacy of a Partially Linear Model Model Checking for Multivariate Regression Models Heteroscedasticity Tests for Regressions Checking the Adequacy of a Varying-Coefficients Model On the Mean Residual Life Regression Model Homegeneity Testing for Covariance Matrices | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a A fundamental issue in statistical analysis is testing the fit of a particular probability model to a set of observed data. Monte Carlo approximation to the null distribution of the test provides a convenient and powerful means of testing model fit. Nonparametric Monte Carlo Tests and Their Applications proposes a new Monte Carlo-based methodology to construct this type of approximation when the model is semistructured. When there are no nuisance parameters to be estimated, the nonparametric Monte Carlo test can exactly maintain the significance level, and when nuisance parameters exist, this method can allow the test to asymptotically maintain the level. The author addresses both applied and theoretical aspects of nonparametric Monte Carlo tests. The new methodology has been used for model checking in many fields of statistics, such as multivariate distribution theory, parametric and semiparametric regression models, multivariate regression models, varying-coefficient models with longitudinal data, heteroscedasticity, and homogeneity of covariance matrices. This book will be of interest to both practitioners and researchers investigating goodness-of-fit tests and resampling approximations. Every chapter of the book includes algorithms, simulations, and theoretical deductions. The prerequisites for a full appreciation of the book are a modest knowledge of mathematical statistics and limit theorems in probability/empirical process theory. The less mathematically sophisticated reader will find Chapters 1, 2 and 6 to be a comprehensible introduction on how and where the new method can apply and the rest of the book to be a valuable reference for Monte Carlo test approximation and goodness-of-fit tests. Lixing Zhu is Associate Professor of Statistics at the University of Hong Kong. He is a winner of the Humboldt Research Award at Alexander-von Humboldt Foundation of Germany and an elected Fellow of the Institute of Mathematical Statistics.> | ||
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