Efficient Derivative Codes through Automatic Differentiation and Interface Contraction: An Application in Biostatistics
Hovland P, Bischof C, Spiegelman D, Casella M. Efficient Derivative Codes through Automatic Differentiation and Interface Contraction: An Application in Biostatistics. SIAM Journal On Scientific Computing 1997, 18: 1056-1066. DOI: 10.1137/s1064827595281800.Peer-Reviewed Original ResearchAutomatic differentiationDerivative codeInterface contractionHigh-level structureADIFOR (Automatic Differentiation of Fortran) toolEfficient codeNumber of variablesPerformance figuresComputation of derivativesCodeLittle effortComputationDivided difference approximationsJudicious fashionUsersCase studyChain ruleLikelihood functionErrorSubroutineFully parametric and semi-parametric regression models for common events with covariate measurement error in main study/validation study designs.
Spiegelman D, Casella M. Fully parametric and semi-parametric regression models for common events with covariate measurement error in main study/validation study designs. Biometrics 1997, 53: 395-409. PMID: 9192443, DOI: 10.2307/2533945.Peer-Reviewed Original ResearchConceptsMain study/validation study designsSemi-parametric methodMeasurement error modelSemi-parametric estimatesCovariate measurement errorSemi-parametric regression modelEmpirical considerationsTrading efficiencyError modelInference proceedsConvenient mathematical propertiesMeasurement errorLikelihood functionModel choiceJoint likelihood functionValidation study designMisspecificationStandard theoryNonparametric formFamily of modelsImportant biasParametric resultsModel covariatesRegression modelsChoice