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Model Selection: General Techniques - Stanford University

- p. 1/16 Statistics203:IntroductiontoRegressionan dAnalysisofVarianceModelSelection:Genera lTechniquesJonathanTaylorlTodaylCrudeout lierdetectiontestlBonferronicorrectionlS imultaneousinferencefor lModelselection:goalslModelselection:gen erallModelselection:strategieslPossible criterialMallow sCplAIC& BIClMaximumlikelihoodestimationlAICfora linearmodellSearchstrategieslImplementat ionsinRlCaveats- p. 2/16 TodaynOutlierdetection/ (Some) lModelselection:goalslModelselection:gen erallModelselection:strategieslPossible criterialMallow sCplAIC& BIClMaximumlikelihoodestimationlAICfora linearmodellSearchstrategieslImplementat ionsinRlCaveats- p. 3/16 CrudeoutlierdetectiontestnIf thestudentizedresidualsarelarge:observat ionmay :ifnis large, if we threshold att1 =2;n p 1wewillgetmany outliersby chanceevenif modelis :Bonferronicorrection,thresholdatt1 =2n;n p lModelselection:goalslModelselection:gen erallModelselection:strategieslPossible criterialMallow sCplAIC& BIClMaximumlikelihoodestimationlAICfora linearmodellSearchstrategieslImplementat ionsinRlCaveats- p.

Model selection: goals Model selection: general Model selection: strategies Possible criteria Mallow’s Cp AIC & BIC Maximum likelihood estimation AIC for a linear model Search strategies Implementations in R Caveats - p. 3/16 Crude outlier detection test If the studentized residuals are large: observation may be an outlier.

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