Measurement Error in Nonlinear Models

Discover the intricacies of regression analysis with Measurement Error in Nonlinear Models by Raymond J. Carroll. Published by Taylor & Francis Inc in 2006, this comprehensive second edition spans 484 pages and delves into the analysis strategies for regression models affected by measurement errors. This essential resource covers a range of advanced topics, including Bayesian methods and semiparametric regression, providing readers with valuable insights into the complexities of nonlinear theories. Additionally, the book features a dedicated chapter on generalized linear mixed models, making it an indispensable guide for statisticians and researchers alike. Enhance your understanding of probability and statistics with this authoritative text, perfect for both academic and professional settings.