Optimal Scaling of Mala for Nonlinear Regression

Preprint

8 July 2004

preprint
Published by arXiv in arXiv

https://doi.org/10.48550/arXiv.math/0407132

Abstract

We 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.

Keywords

SIMULATING EFFICIENTLY
NONLINEAR REGRESSION
OPTIMAL SCALING
REGRESSION
DIFFUSION
SAMPLER
MALA
STAT
SOC

All Related Versions

Version 1, 2004-07-08, ArXiv
Published version: The Annals of Applied Probability, 14 (3).

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