**Summary:** "Measurement Error in Nonlinear Models: A Modern Perspective" is a comprehensive textbook that addresses the challenges of measurement error in nonlinear regression models. The book provides a detailed exploration of the effects of measurement error on statistical inference, with a focus on correcting biases in parameter estimation. It covers both classical and functional measurement error models, including methods such as regression calibration and simulation-extrapolation (SIMEX). The text is structured into four main parts: functional modeling, structural modeling, specialized topics, and an overview of key concepts. It includes numerous examples and applications across various fields, such as epidemiology, biostatistics, and environmental science. The book also discusses advanced topics like Bayesian methods, likelihood inference, and mixed models. It is intended for researchers and practitioners who need to understand and apply methods for handling measurement error in nonlinear models. The second edition includes updated material and expanded coverage of topics such as longitudinal data, nonparametric regression, and survival analysis. The book is well-referenced and provides a thorough treatment of the subject, making it an essential resource for those working in statistical modeling with measurement error.**Summary:** "Measurement Error in Nonlinear Models: A Modern Perspective" is a comprehensive textbook that addresses the challenges of measurement error in nonlinear regression models. The book provides a detailed exploration of the effects of measurement error on statistical inference, with a focus on correcting biases in parameter estimation. It covers both classical and functional measurement error models, including methods such as regression calibration and simulation-extrapolation (SIMEX). The text is structured into four main parts: functional modeling, structural modeling, specialized topics, and an overview of key concepts. It includes numerous examples and applications across various fields, such as epidemiology, biostatistics, and environmental science. The book also discusses advanced topics like Bayesian methods, likelihood inference, and mixed models. It is intended for researchers and practitioners who need to understand and apply methods for handling measurement error in nonlinear models. The second edition includes updated material and expanded coverage of topics such as longitudinal data, nonparametric regression, and survival analysis. The book is well-referenced and provides a thorough treatment of the subject, making it an essential resource for those working in statistical modeling with measurement error.