The Springer Series in Statistics includes a list of advisors and publications. The book "Smoothing Methods in Statistics" by Jeffrey S. Simonoff is a comprehensive guide to smoothing techniques in statistics. It covers various methods such as density estimation, nonparametric regression, and categorical data smoothing. The book is intended for data analysts and statisticians, providing both theoretical and practical insights. It includes a wide range of topics, from simple univariate density estimation to multivariate density estimation and nonparametric regression. The book also discusses computational issues and provides exercises with a strong computational focus. It includes references to over 750 sources and is suitable for use as a textbook for senior undergraduate or graduate-level courses. The book addresses the importance of smoothing methods in data analysis, highlighting their practical applications and the need for data analysts to understand and utilize these methods effectively. It also acknowledges the contributions of other authors in the field of smoothing methods. The book is accompanied by a World Wide Web archive providing additional resources and information. The author emphasizes the importance of applying smoothing methods to real data and provides sources of code for various methods. The book is structured with an introduction, chapters on various topics, and appendices with descriptions of data sets and more on computational issues. The book is a valuable resource for anyone interested in smoothing methods in statistics.The Springer Series in Statistics includes a list of advisors and publications. The book "Smoothing Methods in Statistics" by Jeffrey S. Simonoff is a comprehensive guide to smoothing techniques in statistics. It covers various methods such as density estimation, nonparametric regression, and categorical data smoothing. The book is intended for data analysts and statisticians, providing both theoretical and practical insights. It includes a wide range of topics, from simple univariate density estimation to multivariate density estimation and nonparametric regression. The book also discusses computational issues and provides exercises with a strong computational focus. It includes references to over 750 sources and is suitable for use as a textbook for senior undergraduate or graduate-level courses. The book addresses the importance of smoothing methods in data analysis, highlighting their practical applications and the need for data analysts to understand and utilize these methods effectively. It also acknowledges the contributions of other authors in the field of smoothing methods. The book is accompanied by a World Wide Web archive providing additional resources and information. The author emphasizes the importance of applying smoothing methods to real data and provides sources of code for various methods. The book is structured with an introduction, chapters on various topics, and appendices with descriptions of data sets and more on computational issues. The book is a valuable resource for anyone interested in smoothing methods in statistics.