ESTIMATION UNDER MODEL UNCERTAINTY

被引：3

作者：

Longford, Nicholas T. ^{[1
,2
]}

机构：

[1] SNTL, London, England

[2] Imperial Coll, London, England

来源：

STATISTICA SINICA | 2017年 / 27卷 / 02期

关键词：

Basis estimator; composite estimation; model selection; ordinary regression; propensity matching; SELECTION; LIKELIHOOD;

D O I：

10.5705/ss.202015.0212

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Model selection has had a virtual monopoly on dealing with model uncertainty ever since models were identified as important conduits for statistical inference. Model averaging alleviates some of its deficiencies, but does not offer a practical solution in all settings. We propose an alternative based on linear combinations of the candidate models' estimators. The general proposal is elaborated for ordinary regression and is illustrated with examples. Some estimators based on invalid models contribute to efficient estimation of certain quantities.

引用

页码：859 / 877

页数：19

共 27 条

[1]

[Anonymous], 1993, An introduction to the bootstrap

[2]

[Anonymous], 2006, Model selection and model averaging, DOI DOI 10.1017/CBO9780511790485.003

[3] SCIENCE AND STATISTICS [J].