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Qi-Man Shao has written 3 work(s)
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Hardcover:
9783642150067 | Springer Verlag, October 27, 2010, cover price $74.95
Paperback:
9783642265655, titled "Normal Approximation by Steinâs Method" | Springer Verlag, December 1, 2012, cover price $49.99
Product Description: Self-normalized processes are of common occurrence in probabilistic and statistical studies. A prototypical example is Student's t-statistic introduced in 1908 by Gosset, whose portrait is on the front cover. Due to the highly non-linear nature of these processes, the theory experienced a long period of slow development...read more
Hardcover:
9783540856351 | Springer Verlag, February 3, 2009, cover price $89.99 | About this edition: Self-normalized processes are of common occurrence in probabilistic and statistical studies.
Paperback:
9783642099267 | Springer Verlag, January 28, 2009, cover price $89.99
Product Description: Dealing with methods for sampling from posterior distributions and how to compute posterior quantities of interest using Markov chain Monte Carlo (MCMC) samples, this book addresses such topics as improving simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, highest posterior density interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models...read more (view table of contents, read Amazon.com's description)
Hardcover:
9780387989358 | Springer Verlag, January 1, 2000, cover price $189.00 | About this edition: Dealing with methods for sampling from posterior distributions and how to compute posterior quantities of interest using Markov chain Monte Carlo (MCMC) samples, this book addresses such topics as improving simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, highest posterior density interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models.
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