Monte Carlo Statistical Methods - Couverture rigide

Casella, George; Robert, Christian P.

Couverture rigide

ISBN 10 : 038798707X ISBN 13 : 9780387987071

Editeur : Springer-Verlag New York Inc., 1999

Synopsis

Monte Carlo statistical methods, particularly those based on Markov chains, have now matured to be part of the standard set of techniques used by statisticians. Written as a self-contained logical development of the subject, this book will be suitable as an introduction to the field or as a textbook intended for a seconcl-year graduate course. The reader is not assumed to have any familiarity with Monte Carlo techniques (such as random variable generation), or with any Markov chain theory. Chapters 1 to 3 are introductory, first reviewing various statistical methodologies, then covering the basics of random variable generation and Monte Carlo integration. Chapter 4 is an introduction to Markov chain theory, and Chapter 5 provides the first application of Markov chains to optimization problems. Chapters 6 and 7 cover the heart of MCMC methodology, the Metropolis-Hastings algorithm, and the Gibbs sampler. Finally, Chapter 8 presents methods for monitoring convergence of the MCMC methods, while Chapter 9 shows how these methods apply to some statistical settings that cannot be processed otherwise. Each chapter concludes with a section of notes that serve to enhance the discussion in the chapters.

A propos du livre

À propos de l?auteur

Christian P. Robert is Professor of Statistics in the Mathematics Department at the University of Rouen, France. He is also Head of the Statistics Laboratory at the Center for Research in Economics and Statistics (CREST) of the National Institute for Statistics and Economic Studies (INSEE) in Paris, and lecturer at Ecole Polytechnique. He has written three other books, including The Bayesion Choice, Springer, 1994. He also edited Discretization and MCMC Convergence Assessment, Springer, 1998. He has served as an associate editor for the Annals of Statistics and the Journal of the American Statistical Association. He is a fellow of the Institute of Mathematical Statistics, and a winner of the Young Statistician Award of the So66t6 de Statistique de Paris in 1995. George Casella is the liberty Hyde Bailey Professor of Biological Statistics in the College of Agriculture and life Sciences at Cornell University. He has served as the Theory and Methods Editor of the Journal of the American Statistical Association. He has authored three other textbooks: Statistical Inference, 1990, with Roger L. Berger; Variance Components, 1992, with Shayle R. Searle and Charles E. McCulloch; and Theory of Point Estimation, Second Edition, 1998, with Erich Lehmann. He is a fellow of the Institute of Mathematical Statistics and the American Statistical Association, and an elected fellow of the International Statistical Institute.

Détails de l'édition

• Éditeur: Springer-Verlag New York Inc.
• Date d'édition: 1999
• ISBN 10: 038798707X
• ISBN 13: 9780387987071
• Reliure: Relié
• Langue: anglais
• Nombre de pages: 528
• Coordonnées du fabricant: non disponible