TY - BOOK ID - 185110 TI - Monte Carlo statistical methods AU - Robert, Christian P. AU - Casella, Georges PY - 1999 SN - 038798707X 147573073X 1475730713 9780387987071 PB - New York (N.Y.): Springer, DB - UniCat KW - Mathematical statistics KW - Mathematical statistics. KW - Monte Carlo method. KW - Statistique mathématique KW - Monte-Carlo, Méthode de KW - Monte Carlo method KW - 519.245 KW - 681.3*G3 KW - 519.2 KW - #ABIB:astp KW - 519.5 KW - Artificial sampling KW - Model sampling KW - Monte Carlo simulation KW - Monte Carlo simulation method KW - Stochastic sampling KW - Games of chance (Mathematics) KW - Mathematical models KW - Numerical analysis KW - Numerical calculations KW - Stochastic processes KW - Mathematics KW - Statistical inference KW - Statistics, Mathematical KW - Statistics KW - Probabilities KW - Sampling (Statistics) KW - Stochastic approximation. Monte Carlo methods KW - Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) KW - Probability. Mathematical statistics KW - Statistical methods KW - 519.2 Probability. Mathematical statistics KW - 681.3*G3 Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) KW - 519.245 Stochastic approximation. Monte Carlo methods KW - Statistique mathématique KW - Monte-Carlo, Méthode de KW - Statistics . KW - Statistical Theory and Methods. KW - Statistical analysis KW - Statistical data KW - Statistical science KW - Econometrics KW - Statistiques UR - https://www.unicat.be/uniCat?func=search&query=sysid:185110 AB - 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. This book is intended to bring these techniques into the class­ room, being (we hope) a self-contained logical development of the subject, with all concepts being explained in detail, and all theorems, etc. having detailed proofs. There is also an abundance of examples and problems, re­ lating the concepts with statistical practice and enhancing primarily the application of simulation techniques to statistical problems of various dif­ ficulties. This is a textbook intended for a second-year graduate course. We do not assume that the reader has any familiarity with Monte Carlo techniques (such as random variable generation) or with any Markov chain theory. We do assume that the reader has had a first course in statistical theory at the level of Statistical Inference by Casella and Berger (1990). Unfortu­ nately, a few times throughout the book a somewhat more advanced no­ tion is needed. We have kept these incidents to a minimum and have posted warnings when they occur. While this is a book on simulation, whose actual implementation must be processed through a computer, no requirement is made on programming skills or computing abilities: algorithms are pre­ sented in a program-like format but in plain text rather than in a specific programming language. (Most of the examples in the book were actually implemented in C, with the S-Plus graphical interface. ER -