Geometric Ergodicity of van Dyk and Meng's Algorithm for the Multivariate Student's t Model

By Dobrin Marchev, James P. Hobert. 2004

56 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5b63a2e6e90d064978d089c51f057cc69210eefb

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Citations in the corpus

Sufficient burn-in for Gibbs samplers for a hierarchical random effects model (2004, background): “, Meyn and Tweedie (1994), Rosenthal (1995a), Cowles and Rosenthal (1998), Roberts and Tweedie (1999) and Douc, Moulines and Rosenthal (2002)] have recently provided results that allow one to calculate n when X is geometrically ergodic.”
Multivariate output analysis for Markov chain Monte Carlo (2015, background): “…Jarner and Hansen (2000), Jarner and Roberts (2002), Johnson et al. (2013), Johnson and Jones (2015), Jones et al. (2014), Jones and Hobert (2004), Khare and Hobert (2013), Marchev and Hobert (2004) Roberts and Polson (1994), Tan et al. (2013), Tan and Hobert (2012), Vats (2016), among many others.”
Batch means and spectral variance estimators in Markov chain Monte Carlo (2008, background): “Roy and Hobert [47] prove that this sampler is geometrically ergodic.”
Gibbs sampling, exponential families and orthogonal polynomials (2008, methodology): “M E ] 2 8 A ug 2 00 8 Key words and phrases: Gibbs sampler, running time analyses, exponential families, conjugate priors, location families, orthogonal polynomials, singular value decomposition.”
Fixed-Width Output Analysis for Markov Chain Monte Carlo (2006, background): “(2004), Marchev and Hobert (2004) , Mira and Tierney (2002), Robert (1995), Roberts and Polson”
Markov chain Monte Carlo: Can we trust the third significant figure? (2007, background): “For example, Hobert and Geyer (1998), Johnson and Jones (2008), Jones and Hobert (2004), Marchev and Hobert (2004), Mira and Tierney (2002), Robert (1995), Roberts and Polson (1994), Roberts and Rosenthal (1999), Rosenthal (1995, 1996), Roy and Hobert (2007) and Tierney (1994) examined Gibbs…”
To Center or Not to Center: That Is Not the Question—An Ancillarity–Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Efficiency (2011, result): “Such results have generated some general interests (e.g., Hobert 2001a, 2001b; Lavine 2003; Marchev and Hobert 2004; Hobert and Marchev 2008).”
On the Markov chain central limit theorem (2004, background): “Several m otivating exam ples are given which range from toy one-dim ensionalsettings to com plicated settingsencountered in M arkov chain M onte Carlo.”