Simulation in Statistics

Simulation has become a standard tool in statistics because it may be the only tool available for analysing some classes of probabilistic models. We review in this paper simulation tools that have been specifically derived to address statistical challenges and, in particular, recent advances in the areas of adaptive Markov chain Monte Carlo (MCMC) algorithms, and approximate Bayesian calculation (ABC) algorithms.

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Year of publication:	2011
Authors:	Robert, Christian P.
Institutions:	Université Paris-Dauphine (Paris IX)
Subject:	importance sampling \| SMC \| ABC \| MCMC \| Markov chains \| bootstrap \| maximum likelihood \| Bayesian statistics \| posterior distributions \| Bayes factor \| Monte Carlo \| adaptivity \| model choice

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Series:	Economics Papers from University Paris Dauphine.
Type of publication:	Book / Working Paper
Notes:	Published in Winter Simulation Conference, WSC 2011. Proceedings : 11-14 December 2011, Phoenix, Arizona,
Classification:	C15 - Statistical Simulation Methods; Monte Carlo Methods ; C11 - Bayesian Analysis
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010707776