Tools for statistical inference. Methods for the exploration of posterior distributions and likelihood functions. 2. ed. (English) Zbl 0777.62003

Tanner, Martin A.

Springer Series in Statistics. New York: Springer-Verlag. ix, 156 p. (1993).

From the preface: In this second edition [for a review of the first edition (1991), see Zbl 0724.62003], I have attempted to expand the treatment of many of the techniques discussed, as well as included important topics such as the Metropolis algorithm and methods for assessing the convergence of Markov chain algorithms.

MSC:

62-02	Research exposition (monographs, survey articles) pertaining to statistics
65C60	Computational problems in statistics (MSC2010)
62F12	Asymptotic properties of parametric estimators
62F15	Bayesian inference
62F25	Parametric tolerance and confidence regions

Keywords:

observed data methods; data augmentation methods; censored regression data; random randomized response; latent class analysis; hierarchical models; likelihood function; posterior density; maximum likelihood method; normal based inference; highest posterior density region; significance level; approximations; numerical integration; Laplace expansions; Monte Carlo; composition; importance sampling; predictive distribution; EM algorithm; Gibbs sampler; latent data; normal approximation; Poor Man’s data augmentation algorithm; non-normal approximation; iterative algorithms; SIR algorithm; Metropolis algorithm; convergence of Markov chain algorithms

Citations:

Zbl 0724.00003; Zbl 0724.62003