Fitting Statistical models to data with many more variables than ...

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Spurious results – “sparse is good”Linear regressionRandom yReal Xn=32 p=14000+apparent Rsq0.0 0.2 0.4 0.6 0.8 1.0Boxplot1 3 5 7 9 11 14 17 20 23 26 29 32 35 38subset size
Cross validationBoxplotC ross validated rsq0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.71 3 5 7 9 11 14 17 20 23 26 29 32 35 38subset size
Page 2: Talk outline1. Introduction, motiva
Page 5: IntroductionResponse ModellingEach
Page 9: Some issues1. Artefacts - apparent
Page 13 and 14: Parameters in the modelsThe models
Page 15 and 16: The prior - special casesk=1 (Lasso
Page 21 and 22: An EM algorithmWant to find (local)
Page 23 and 24: E- step continuedThe conditional ex
Page 25 and 26: CommentsStructure can be used to ma
Page 27 and 28: GeneRave in Action
Page 29 and 30: Êÿëáÿúÿð ÿðàçèñèíä
Page 31 and 32: Example 1: St Jude’s Leukaemia da
Page 33 and 34: Single Nucleotide PolymorphismsSNPA
Page 35 and 36: Data and modelWe fit a sparse “ma
Page 37 and 38: Two SNP race classifierafd0860639
Page 39 and 40: Independent validation of resultsTh
Page 41 and 42: CodeR Library available for downloa

Fitting Statistical models to data with many more variables than ...

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