Geometric ergodicity of a random-walk Metropolis algorithm for a transformed density
2010-11-22
Loading...
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Geometric ergodicity of a random-walk Metropolis algorithm for a transformed density
Authors
Published Date
2010-11-22
Publisher
Type
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.
Description
Related to
Replaces
License
Series/Report Number
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Other identifiers
Suggested citation
Johnson, Leif; Geyer, Charles J.. (2010). Geometric ergodicity of a random-walk Metropolis algorithm for a transformed density. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/96959.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.