Optimal Subsampling for Large Sample Logistic Regression

For massive data, the family of subsampling algorithms is popular to downsize the data volume and reduce computational burden. Existing studies focus on approximating the ordinary least-square estimate in linear regression, where statistical leverage scores are often used to define subsampling proba...

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Veröffentlicht in:	Journal of the American Statistical Association Jg. 113; H. 522; S. 829 - 844
Hauptverfasser:	Wang, HaiYing, Zhu, Rong, Ma, Ping
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Taylor & Francis 03.04.2018 Taylor & Francis Group,LLC Taylor & Francis Ltd
Schlagworte:	A-optimality Algorithms Asymptotic properties Computational efficiency Computing costs Computing time Consistency Data data collection equations Logistic regression Massive data Maximum likelihood estimates Minimization Normality Optimal subsampling Optimization Probability Rare event Regression Regression analysis Statistical analysis Statistical methods Statistics Theory and Methods Rare Event Logistic Regression Optimal Subsampling A-optimality Massive Data
ISSN:	0162-1459, 1537-274X, 1537-274X
Online-Zugang:	Volltext
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