Titre : | Nonparametric Monte Carlo tests and their applications | Type de document : | texte imprimé | Auteurs : | Lixing ZHU, Auteur | Editeur : | Springer Verlag | Année de publication : | Cop. 2005 | Collection : | Lecture notes in statistics num. 182 | Importance : | XI-181 p. | ISBN/ISSN/EAN : | 978-0-387-25038-0 | Langues : | Anglais | Mots-clés : | test de Monte Carlo | Résumé : | 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. | Note de contenu : | index, références |
Nonparametric Monte Carlo tests and their applications [texte imprimé] / Lixing ZHU, Auteur . - [S.l.] : Springer Verlag, Cop. 2005 . - XI-181 p.. - ( Lecture notes in statistics; 182) . ISBN : 978-0-387-25038-0 Langues : Anglais Mots-clés : | test de Monte Carlo | Résumé : | 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. | Note de contenu : | index, références |
| ![Nonparametric Monte Carlo tests and their applications vignette](https://math22.math.univ-montp2.fr/images/vide.png) |