Measurement error in nonlinear models: a modern perspective

摘要: Guide to Notation Introduction The Double/Triple-Whammy of Measurement Error Classical A Nutrition Example Examples Radiation Epidemiology and Berkson Errors Model Extensions Other Models Checking Loss Power Brief Tour Bibliographic Notes Important Concepts Functional Structural for Sources Data Is There an "Exact" Predictor? What is Truth? Differential Nondifferential Prediction Linear Regression Attenuation Bias Caused by Multiple Orthogonal Correcting Versus Variance in General Problems Calibration Overview Algorithm NHANES Estimating the Function Parameters Multiplicative Standard Expanded Approximations Theoretical Software Simulation Extrapolation Heuristics SIMEX Applications Some Special Cases Related Methods Instrumental Variables Approximate Variable Estimation Adjusted Score Method Methodologies Logistic Conditional Functions Corrected Computation Asymptotic Comparison Scores Likelihood Quasilikelihood Steps 2 3: Constructing Likelihoods Step 4: Numerical Cervical Cancer Herpes Framingham Nevada Test Site Reanalysis Bronchitis Bayesian Gibbs Sampler Metropolis-Hastings Nonlinear Automatic implementation OPEN Data: Components Hypothesis Testing Approximation Illustration: Hypotheses about Sub-Vectors ssx ssz Efficient Tests H0 : = 0 Longitudinal Mixed Estimator GLMMEMs GLMMs Maximum Joint Modeling Example: CHOICE Study Nonparametric Deconvolution Baseline Change Semiparametric Additive MCMC Spline Monte-Carlo EM-Algorithm with Semiparametrics: X Modeled Parametrically Parametric Models: No Assumptions on Survival Induced Hazard Analysis Chronic Kidney Disease Progression Semi Inference Frailty Response Forms Use Complete Only Validation Appendix A: Background Material Normal Lognormal Distributions Gamma Inverse Best Unbiased Equations (QVF) Generalized Bootstrap B: Technical Details Chapter 1: References Index