Multiple Imputation and Random Forests (MIRF) for unobservable, high-dimensional data

International Journal of Biostatistics

Volumn 3, Issue 1, 2007, Pages

  Nonyane, Bareng A S ^a   Foulkes, Andrea S ^a  

^a UNIVERSITY OF MASSACHUSETTS   (United States)

Author keywords

Genotype; Haplotype; HIV 1; Lipids; Phase; Random forests; Recursive partitioning

Indexed keywords

ANTIRETROVIRUS AGENT; ANTI HUMAN IMMUNODEFICIENCY VIRUS AGENT; APOLIPOPROTEIN; TRIACYLGLYCEROL LIPASE;

ALLELE; ANALYTIC METHOD; ARTICLE; CARDIOVASCULAR DISEASE; CHROMOSOME; COHORT ANALYSIS; DISEASE COURSE; DYSLIPIDEMIA; GENETIC ANALYSIS; GENOTYPE; HAPLOTYPE; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; MAJOR CLINICAL STUDY; PREDICTION; SIMULATION; SINGLE NUCLEOTIDE POLYMORPHISM; STATISTICAL ANALYSIS; BIOLOGICAL MODEL; CHEMICALLY INDUCED DISORDER; COMPUTER SIMULATION; ENZYMOLOGY; GENETICS; HUMAN IMMUNODEFICIENCY VIRUS 1;

HUMAN IMMUNODEFICIENCY VIRUS 1;

ANTI-HIV AGENTS; APOLIPOPROTEINS; COHORT STUDIES; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; DYSLIPIDEMIAS; HAPLOTYPES; HIV INFECTIONS; HIV-1; HUMANS; LIPASE; MODELS, GENETIC; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 34548133733     PISSN: 15574679     EISSN: None     Source Type: Journal    
DOI: 10.2202/1557-4679.1049     Document Type: Article

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