The fitting of complex parametric models

• 成果类型: Article
• 署名作者: Cox, D. R.; Kartsonaki, Christiana
• 署名单位: University of Oxford
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/ass030
• 发表日期: 2012
• 页码: 741747
• 关键词: approximate bayesian computation; inference

摘要

Consider parametric models that are too complicated to allow calculation of a likelihood but from which observations can be simulated. We examine parameter estimators that are linear functions of a possibly large set of candidate features. A combination of simulations based on a fractional design and sets of discriminant analyses is then used to find an optimal estimator of the vector parameter and its covariance matrix. The procedure is an alternative to the approximate Bayesian computation scheme.