Markov chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models

By Sylvia Frühwirth-Schnatter. 2001

503 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/762e621e6c59d6d8e92b065c2d307dfdde954aa7

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Citations in the corpus

OPTIMAL TEST FOR MARKOV SWITCHING PARAMETERS (2014, background): “Bayesian inference for Markov-switching models is an active research area, see e.g. Chib (1996), Kim and Nelson (1999), Frühwirth-Schnatter (2001).”
Bayesian Analysis of the Heterogeneity Model (2004, methodology): “One may apply the idea of Frühwirth-Schnatter (2001a) to explore simulations from the unconstrained posterior in order to find an identifiability constraintR which is able to separate the posterior modes and by this to identify simulations from one labeling subspace, say L .”
Mixture of Regression Models With Varying Mixing Proportions: A Semiparametric Approach (2012, background): “, in econometrics (Wedel and DeSarbo, 1993; Frühwirth-Schnatter, 2001), and in biology and epidemiology (Wang et al.”
Handbook of Markov Chain Monte Carlo: Hardcover: 619 pages Publisher: Chapman and Hall/CRC Press (first edition, May 2011) Language: English ISBN-10: 1420079417 (2012, background): “Furthermore, suitable constraints can be difficult or almost impossible to find (Frühwirth-Schnatter, 2001).”
Estimating the Marginal Likelihood Using the Arithmetic Mean Identity (2017, methodology): “Because the Gibbs sampler may not explore the whole unconstrained parameter space, but tends to stick to one posterior mode with occasional switches among other posterior modes (as a consequence, some of the posterior modes are hardly ever or just never “visited”), the random permutation sampler (in”
Normalization in Econometrics (2007, background): “1This observation that simply restricting θ to an identified subspace is not a satisfactory solution to the label-switching problem has also been forcefully made by Celeux, Hurn and Rober (2000), Stephens (2000), and Frühwirth-Schnatter (2001), though none of them interpret this problem in terms of ”
K-state switching models with time-varying transition distributions—Does loan growth signal stronger effects of variables on inflation? (2015, methodology): “This renders the prior invariant to state permutations and allows to explore the full unconditional posterior distribution of the parameters using the random permutation sampler (Frühwirth-Schnatter 2001).”