Accession Number : ADP007124

Title :   A Comparison of Approaches to Inference for Nonlinear Models,

Corporate Author : WISCONSIN UNIV-MADISON DEPT OF INDUSTRIAL ENGINEERING

Personal Author(s) : Ritter, Christian ; Bisgaard, Soeren ; Bates, Douglas

Report Date : 1992

Pagination or Media Count : 8

Abstract : As greater computing power becomes routinely available to researchers, analyses based on Bayesian or likelihood methods become easier to perform, especially since the increase in computing power has been accompanied by development of inventive statistical algorithms for inference. We consider here the nonlinear regression model but these approaches to inference are applicable in more general circumstances and we feel the comparisons will remain useful. Several methods can be used for inference in nonlinear regression: propagation of errors, likelihood profiles, approximate marginal likelihoods and posteriors, and Monte Carlo methods such as importance sampling, and the Gibbs sampler. These methods vary in computing intensity and in their ability to handle poorly conditioned situations. Furthermore, since some of these methods have only been recently developed, it is not easy for the practitioner to compare them and choose between them because they are not widely implemented. We demonstrate the respective merits of these methods in a small but instructive example. Nonlinear Models; Profile Likelihood; Importance Sampling; Gibbs Sampler, Approximate Marginalization.

Descriptors :   *ALGORITHMS, *PROFILES, *COMPUTER PROGRAMS, APPROACH, COMPARISON, ERRORS, INTENSITY, MODELS, POWER, SAMPLERS, SAMPLING.

Subject Categories : Computer Programming and Software

Distribution Statement : APPROVED FOR PUBLIC RELEASE