The fitting of complex parametric models

被引：3
|
作者：
Cox, D. R. [1 ]
Kartsonaki, Christiana [1 ]
机构：
[1] Univ Oxford Nuffield Coll, Oxford OX1 1NF, England
来源：
BIOMETRIKA | 2012年 / 99卷 / 03期
关键词：
Approximate Bayesian computation; Discriminant analysis; Generalized method of moments; Optimal parametric estimation; Plackett-Burman design; Simulation; APPROXIMATE BAYESIAN COMPUTATION; INFERENCE;
D O I：
10.1093/biomet/ass030
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
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.
引用
收藏
页码：741 / 747
页数：7
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