Optimal Scaling of Mala for Nonlinear Regression

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dc.creatorBreyer, Laird Arnault
dc.creatorPiccioni, Mauro
dc.creatorScarlatti, Sergio
dc.date2004-07-08
dc.date.accessioned2026-07-07T05:10:05Z
dc.date.available2026-07-07T05:10:05Z
dc.descriptionWe address the problem of simulating efficiently from the posterior distribution over the parameters of a particular class of nonlinear regression models using a Langevin-Metropolis sampler. It is shown that as the number N of parameters increases, the proposal variance must scale as N{-1/3} in order to converge to a diffusion. This generalizes previous results of Roberts and Rosenthal [J. R. Stat. Soc. Ser. B Stat. Methodol. 60 (1998) 255-268] for the i.i.d. case, showing the robustness of their analysis.
dc.identifierhttps://arxiv.org/abs/math/0407132
dc.identifierhttp://arxiv.org/abs/math/0407132
dc.identifierAnnals of Probability 2004, Vol. 14, No. 3, 1479-1505
dc.identifierdoi:10.1214/105051604000000369
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/71818
dc.subjectProbability
dc.subject60F17 (Primary) 60F05, 60F10 (Secondary)
dc.titleOptimal Scaling of Mala for Nonlinear Regression
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