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Measurement Error in Nonlinear Models: A Modern Perspective

By: RJ Carroll, David Ruppert, Leonard A Stefanski and Ciprian Crainiceanu

455 pages, Figs

Chapman & Hall (CRC Press)

Hardback | Dec 2006 | Edition: 2 | #159423 | ISBN: 1584886331
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About this book

It's been over a decade since the first edition of Measurement Error in Nonlinear Models splashed onto the scene, and research in the field has certainly not cooled in the interim. In fact, quite the opposite has occurred. As a result, Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition has been revamped and extensively updated to offer the most comprehensive and up-to-date survey of measurement error models currently available.

What's new in the Second Edition?
* Greatly expanded discussion and applications of Bayesian computation via Markov Chain Monte Carlo techniques
* A new chapter on longitudinal data and mixed models
* A thoroughly revised chapter on nonparametric regression and density estimation
* A totally new chapter on semiparametric regression
* Survival analysis expanded into its own separate chapter
* Completely rewritten chapter on score functions
* Many more examples and illustrative graphs
* Unique data sets compiled and made available online

In addition, the authors expanded the background material in Appendix A and integrated the technical material from chapter appendices into a new Appendix B for convenient navigation. Regardless of your field, if you're looking for the most extensive discussion and review of measurement error models, then Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition is your ideal source.


Guide to NotationINTRODUCTIONThe Double/Triple-Whammy of Measurement ErrorClassical Measurement Error A Nutrition ExampleMeasurement Error ExamplesRadiation Epidemiology and Berkson ErrorsClassical Measurement Error Model ExtensionsOther Examples of Measurement Error ModelsChecking The Classical Error ModelLoss of PowerA Brief TourBibliographic NotesIMPORTANT CONCEPTSFunctional and Structural ModelsModels for Measurement ErrorSources of DataIs There an "Exact" Predictor? What is Truth?Differential and Nondifferential ErrorPredictionBibliographic NotesLINEAR REGRESSION AND ATTENUATIONIntroductionBias Caused by Measurement ErrorMultiple and Orthogonal RegressionCorrecting for BiasBias Versus VarianceAttenuation in General ProblemsBibliographic NotesREGRESSION CALIBRATIONOverviewThe Regression Calibration AlgorithmNHANES ExampleEstimating the Calibration Function ParametersMultiplicative Measurement ErrorStandard ErrorsExpanded Regression Calibration ModelsExamples of the ApproximationsTheoretical ExamplesBibliographic Notes and SoftwareSIMULATION EXTRAPOLATIONOverviewSimulation Extrapolation HeuristicsThe SIMEX AlgorithmApplicationsSIMEX in Some Important Special CasesExtensions and Related MethodsBibliographic NotesINSTRUMENTAL VARIABLESOverviewInstrumental Variables in Linear ModelsApproximate Instrumental Variable EstimationAdjusted Score MethodExamplesOther MethodologiesBibliographic NotesSCORE FUNCTION METHODSOverviewLinear and Logistic RegressionConditional Score FunctionsCorrected Score FunctionsComputation and Asymptotic ApproximationsComparison of Conditional and Corrected ScoresBibliographic NotesLIKELIHOOD AND QUASILIKELIHOODIntroductionSteps 2 and 3: Constructing LikelihoodsStep 4: Numerical Computation of LikelihoodsCervical Cancer and HerpesFramingham DataNevada Test Site ReanalysisBronchitis ExampleQuasilikelihood and Variance Function ModelsBibliographic NotesBAYESIAN METHODSOverviewThe Gibbs SamplerMetropolis-Hastings AlgorithmLinear RegressionNonlinear ModelsLogistic RegressionBerkson ErrorsAutomatic implementationCervical Cancer and HerpesFramingham DataOPEN Data: A Variance Components ModelBibliographic NotesHYPOTHESIS TESTINGOverviewThe Regression Calibration ApproximationIllustration: OPEN DataHypotheses about Sub-Vectors of x and zEfficient Score Tests of H0 : x = 0Bibliographic NotesLONGITUDINAL DATA AND MIXED MODELSMixed Models for Longitudinal DataMixed Measurement Error ModelsA Bias Corrected EstimatorSIMEX for GLMMEMsRegression Calibration for GLMMsMaximum Likelihood EstimationJoint ModelingOther Models and ApplicationsExample: The CHOICE StudyBibliographic NotesNONPARAMETRIC ESTIMATIONDeconvolutionNonparametric RegressionBaseline Change ExampleBibliographic NotesSEMIPARAMETRIC REGRESSIONOverviewAddi

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