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SURE INDEPENDENCE SCREENING IN GENERALIZED LINEAR MODELS WITH NP-DIMENSIONALITY
by
Song, Rui
, Fan, Jianqing
in
60F10
/ 62E99
/ 62J12
/ 68Q32
/ Correlation analysis
/ Covariance matrices
/ Dimensional analysis
/ Estimators
/ Exact sciences and technology
/ General topics
/ Generalized linear model
/ Generalized linear models
/ independent learning
/ Linear models
/ Linear regression
/ Logistic regression
/ Markov processes
/ Mathematics
/ Modeling
/ Multivariate analysis
/ Parametric inference
/ Probability and statistics
/ Probability theory and stochastic processes
/ Random variables
/ Regression analysis
/ Sciences and techniques of general use
/ Simulations
/ Statistics
/ Studies
/ sure independent screening
/ variable selection
2010
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SURE INDEPENDENCE SCREENING IN GENERALIZED LINEAR MODELS WITH NP-DIMENSIONALITY
by
Song, Rui
, Fan, Jianqing
in
60F10
/ 62E99
/ 62J12
/ 68Q32
/ Correlation analysis
/ Covariance matrices
/ Dimensional analysis
/ Estimators
/ Exact sciences and technology
/ General topics
/ Generalized linear model
/ Generalized linear models
/ independent learning
/ Linear models
/ Linear regression
/ Logistic regression
/ Markov processes
/ Mathematics
/ Modeling
/ Multivariate analysis
/ Parametric inference
/ Probability and statistics
/ Probability theory and stochastic processes
/ Random variables
/ Regression analysis
/ Sciences and techniques of general use
/ Simulations
/ Statistics
/ Studies
/ sure independent screening
/ variable selection
2010
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SURE INDEPENDENCE SCREENING IN GENERALIZED LINEAR MODELS WITH NP-DIMENSIONALITY
by
Song, Rui
, Fan, Jianqing
in
60F10
/ 62E99
/ 62J12
/ 68Q32
/ Correlation analysis
/ Covariance matrices
/ Dimensional analysis
/ Estimators
/ Exact sciences and technology
/ General topics
/ Generalized linear model
/ Generalized linear models
/ independent learning
/ Linear models
/ Linear regression
/ Logistic regression
/ Markov processes
/ Mathematics
/ Modeling
/ Multivariate analysis
/ Parametric inference
/ Probability and statistics
/ Probability theory and stochastic processes
/ Random variables
/ Regression analysis
/ Sciences and techniques of general use
/ Simulations
/ Statistics
/ Studies
/ sure independent screening
/ variable selection
2010
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SURE INDEPENDENCE SCREENING IN GENERALIZED LINEAR MODELS WITH NP-DIMENSIONALITY
Journal Article
SURE INDEPENDENCE SCREENING IN GENERALIZED LINEAR MODELS WITH NP-DIMENSIONALITY
2010
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Overview
Ultrahigh-dimensional variable selection plays an increasingly important role in contemporary scientific discoveries and statistical research. Among others, Fan and Lv [J. R. Stat. Soc. Ser. B Stat. Methodol. 70 (2008) 849—911] propose an independent screening framework by ranking the marginal correlations. They showed that the correlation ranking procedure possesses a sure independence screening property within the context of the linear model with Gaussian covariates and responses. In this paper, we propose a more general version of the independent learning with ranking the maximum marginal likelihood estimates or the maximum marginal likelihood itself in generalized linear models. We show that the proposed methods, with Fan and Lv [J. R. Stat. Soc. Ser. B Stat. Methodol. 70 (2008) 849—911] as a very special case, also possess the sure screening property with vanishing false selection rate. The conditions under which the independence learning possesses a sure screening is surprisingly simple. This justifies the applicability of such a simple method in a wide spectrum. We quantify explicitly the extent to which the dimensionality can be reduced by independence screening, which depends on the interactions of the covariance matrix of covariates and true parameters. Simulation studies are used to illustrate the utility of the proposed approaches. In addition, we establish an exponential inequality for the quasi-maximum likelihood estimator which is useful for high-dimensional statistical learning.
Publisher
Institute of Mathematical Statistics,The Institute of Mathematical Statistics
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