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Please use this identifier to cite or link to this item: http://hdl.handle.net/11299/96959

Title: Geometric ergodicity of a random-walk Metropolis algorithm for a transformed density
Authors: Johnson, Leif
Geyer, Charles J.
Keywords: Markov chain Monte Carlo
random walk
transformed density
geometric ergodicity
Issue Date: 22-Nov-2010
Series/Report no.: Technical Report
Abstract: Curvature conditions on a target density in R^k for the geometric ergodicity of a random-walk Metropolis algorithm have previously been established (Mengersen and Tweedie(1996), Roberts and Tweedie(1996), Jarner and Hansen(2000)). However, the conditions for target densities in R^k that have exponentially light tails, but are not super-exponential are difficult to apply. In this paper I establish a variable transformation to apply to such target densities, that along with a regularity condition on the target density, ensures that a random-walk Metropolis algorithm for the transformed density is geometrically ergodic. Inference can be drawn for the original target density using Markov chain Monte Carlo estimates based on the transformed density. An application to inference on the regression parameter in multinomial logit regression with a conjugate prior is given.
URI: http://purl.umn.edu/96959
Appears in Collections:Graduate Student Research

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