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

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2. A Bayesian (penalised likelihood) approach (p>>n)Posterior ∝ Likelihood x Prior•Linear regression•GLM’s•Survival analysis•Multinomialregression…Strongly favoursparameters = 0Takes care of flat spots, artefacts, computational load insubset selection
Parameters in the modelsThe models have a linear predictor ( η=Xβ)Each variable/gene i has a parameter β i associated with it
Page 2: Talk outline1. Introduction, motiva
Page 5: IntroductionResponse ModellingEach
Page 9 and 10: Some issues1. Artefacts - apparent
Page 11: Cross validationBoxplotC ross valid
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

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