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Noniterative adjustment to regression estimators with population‐based auxiliary information for semiparametric models
Author(s) -
Gao Fei,
Chan K. C. G.
Publication year - 2023
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13585
Subject(s) - estimator , computer science , population , regression , construct (python library) , regression analysis , econometrics , data mining , sample (material) , contrast (vision) , sample size determination , semiparametric regression , statistics , machine learning , mathematics , artificial intelligence , medicine , environmental health , chemistry , chromatography , programming language
Disease registries, surveillance data, and other datasets with extremely large sample sizes become increasingly available in providing population‐based information on disease incidence, survival probability, or other important public health characteristics. Such information can be leveraged in studies that collect detailed measurements but with smaller sample sizes. In contrast to recent proposals that formulate additional information as constraints in optimization problems, we develop a general framework to construct simple estimators that update the usual regression estimators with some functionals of data that incorporate the additional information. We consider general settings that incorporate nuisance parameters in the auxiliary information, non‐ i.i.d . data such as those from case‐control studies, and semiparametric models with infinite‐dimensional parameters common in survival analysis. Details of several important data and sampling settings are provided with numerical examples.