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

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Talk outline1. Introduction, motivation and issues when p>>n2. Response modellingLikelihood <strong>with</strong> sparsity prior (penalty)3. ExamplesMicroarraysSNP Chips
Page 4 and 5: ìÿí ùÿð íàìàçäà Áþé
Page 8 and 9: p>>n some possible approaches1. Ign
Page 10 and 11: Spurious results - “sparse is goo
Page 12 and 13: 2. A Bayesian (penalised likelihood
Page 14 and 15: A sparsity priorDraw ν from p( ν
Page 16: A visual descriptionPosterior ∝ l
Page 22 and 23: E-step1. InitialisationSet intial v
Page 24 and 25: M-stepMaximiseMany possibilities eg
Page 26 and 27: CommentsParameter estimation and va
Page 28 and 29: GeneRave in Action
Page 30 and 31: St Judes Leukemia data - Probes and
Page 32 and 33: Example 2: Perlegen SNP dataReferen
Page 34 and 35: SNP’s are a major determinant of
Page 36 and 37: GeneRave - Perlegen SNP Data1,548,3
Page 38 and 39: Validation data - Hapmap data setht
Page 40 and 41: SNP Analysis Conclusion•The sex S
Page 42: ContactName Harri KiiveriTitle Rese

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