The cross-validated AUC for MCP-logistic regression with high-dimensional data

Statistical Methods in Medical Research

Volumn 22, Issue 5, 2013, Pages 505-518

  Jiang, Dingfeng ^a   Huang, Jian ^a   Zhang, Ying ^a  

^a UNIVERSITY OF IOWA   (United States)

Author keywords

binary outcome; cross validation; high dimensional data; Lasso; minimax concave penalty; tuning parameter selection

Indexed keywords

POLO LIKE KINASE 2; PROTEIN SERINE THREONINE KINASE;

ACUTE LYMPHOBLASTIC LEUKEMIA; AKAIKE INFORMATION CRITERION; ALGORITHM; AREA UNDER THE CURVE; ARTICLE; BAYESIAN INFORMATION CRETERION; BREAST CANCER; CROSS VALIDATED AREA UNDER THE CURVE; EXTENDED BAYESIAN INFORMATION CRITERION; GENE IDENTIFICATION; HUMAN; INTERMETHOD COMPARISON; LOGISTIC REGRESSION ANALYSIS; MAXIMUM LIKELIHOOD METHOD; MICROARRAY ANALYSIS; MINIMAX CONCAVE PENALTY METHOD; NUCLEOTIDE SEQUENCE; PREDICTIVE VALUE; SIMULATION; STATISTICAL ANALYSIS; TUMOR SUPPRESSOR GENE;

BINARY OUTCOME; CROSS-VALIDATION; HIGH-DIMENSIONAL DATA; LASSO; MINIMAX CONCAVE PENALTY; TUNING PARAMETER SELECTION;

AREA UNDER CURVE; LOGISTIC MODELS; MODELS, THEORETICAL;

EID: 84886533098     PISSN: 09622802     EISSN: 14770334     Source Type: Journal    
DOI: 10.1177/0962280211428385     Document Type: Article

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