Estimating exogenous variables in data with more variables than observations

Neural Networks

Volumn 24, Issue 8, 2011, Pages 875-880

  Sogawa, Yasuhiro ^a   Shimizu, Shohei ^a   Shimamura, Teppei ^b   Hyvärinen, Aapo ^c   Washio, Takashi ^a   Imoto, Seiya ^b  

^a OSAKA UNIVERSITY   (Japan)

^b UNIVERSITY OF TOKYO   (Japan)

^c UNIVERSITY OF HELSINKI   (Finland)

Author keywords

Bayesian networks; Data with more variables than observations; Independent component analysis; Non Gaussianity

Indexed keywords

ARTIFICIAL DATA; CAUSAL CHAINS; CAUSAL MODEL; CAUSAL MODELING; CLASSICAL SITUATIONS; CONVENTIONAL METHODS; DATA SETS; DATA WITH MORE VARIABLES THAN OBSERVATIONS; EFFICIENT EXPERIMENTAL DESIGNS; EXOGENOUS VARIABLES; GENE EXPRESSION DATA; HIGH-DIMENSIONAL; INDEPENDENT COMPONENTS; NON-GAUSSIAN; NONGAUSSIANITY; ORDERS OF MAGNITUDE; SAMPLE SIZES;

BAYESIAN NETWORKS; INDEPENDENT COMPONENT ANALYSIS; INFERENCE ENGINES;

GENE EXPRESSION;

ALGORITHM; ARTICLE; CAUSAL MODELING; EXOGENOUS VARIABLE; GENE EXPRESSION; INDEPENDENT COMPONENT ANALYSIS; LINEAR SYSTEM; PRIORITY JOURNAL; SAMPLE SIZE; STATISTICAL MODEL;

ALGORITHMS; CAUSALITY; DATABASES, FACTUAL; DATABASES, GENETIC; LINEAR MODELS; MICROARRAY ANALYSIS; NORMAL DISTRIBUTION; PRINCIPAL COMPONENT ANALYSIS;

EID: 80051781964     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2011.05.017     Document Type: Article

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