The book "Measurement Error in Nonlinear Models: A Modern Perspective" by Raymond J. Carroll, David Ruppert, Leonard A. Stefanski, and Ciprian M. Crainiceanu, in its second edition, provides a comprehensive overview of the analysis of nonlinear regression models with measurement errors. The authors address the challenges and complexities arising from measurement errors in predictors, which can lead to biased estimates and reduced statistical power. The book is divided into four main parts: 1. **Introduction**: Discusses the importance of addressing measurement errors in regression models, provides examples, and introduces basic concepts and terminology. 2. **Functional Modeling**: Focuses on methods that do not parametrically model the distribution of the true predictor, including regression calibration and simulation-extrapolation (SIMEX). These methods are applicable when the true predictor is never observable. 3. **Structural Modeling**: Explores parametric models for the distribution of the true predictor, using likelihood and Bayesian approaches. This part emphasizes the distinction between functional and structural modeling and discusses the measurement error problem as a missing data issue. 4. **Specialized Topics**: Covers advanced topics such as functional techniques for observed predictors, generalized linear models with unknown link functions, hypothesis testing, nonparametric regression, and response variable errors. The book aims to provide a broad and up-to-date coverage of the field, including recent developments in Bayesian computation and Markov Chain Monte Carlo techniques. It is designed to be accessible to readers with varying levels of background knowledge, offering both conceptual explanations and detailed technical discussions. The authors also provide a detailed guide to notation and background material, making it a valuable resource for both researchers and practitioners in statistics and related fields.The book "Measurement Error in Nonlinear Models: A Modern Perspective" by Raymond J. Carroll, David Ruppert, Leonard A. Stefanski, and Ciprian M. Crainiceanu, in its second edition, provides a comprehensive overview of the analysis of nonlinear regression models with measurement errors. The authors address the challenges and complexities arising from measurement errors in predictors, which can lead to biased estimates and reduced statistical power. The book is divided into four main parts: 1. **Introduction**: Discusses the importance of addressing measurement errors in regression models, provides examples, and introduces basic concepts and terminology. 2. **Functional Modeling**: Focuses on methods that do not parametrically model the distribution of the true predictor, including regression calibration and simulation-extrapolation (SIMEX). These methods are applicable when the true predictor is never observable. 3. **Structural Modeling**: Explores parametric models for the distribution of the true predictor, using likelihood and Bayesian approaches. This part emphasizes the distinction between functional and structural modeling and discusses the measurement error problem as a missing data issue. 4. **Specialized Topics**: Covers advanced topics such as functional techniques for observed predictors, generalized linear models with unknown link functions, hypothesis testing, nonparametric regression, and response variable errors. The book aims to provide a broad and up-to-date coverage of the field, including recent developments in Bayesian computation and Markov Chain Monte Carlo techniques. It is designed to be accessible to readers with varying levels of background knowledge, offering both conceptual explanations and detailed technical discussions. The authors also provide a detailed guide to notation and background material, making it a valuable resource for both researchers and practitioners in statistics and related fields.