Generalized Additive Models (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)
| Author | : | |
| Rating | : | 4.98 (997 Votes) |
| Asin | : | 0412343908 |
| Format Type | : | paperback |
| Number of Pages | : | 352 Pages |
| Publish Date | : | 2015-10-07 |
| Language | : | English |
DESCRIPTION:
"nice generalization of linear models" according to Michael R. Chernick. Believe it or not this important topic in applied statistics was developed by Hastie and Tibshirani when they were graduate students, a sure sign of greatness to come. After their graduation this highly acclaimed book came out explaining both the theory and pratice of generalized additive models in a clear and concise way. Generalized additive models are similar to generalized linea. A complex topic made readable. A Customer A medical doctor recommended that I (a statistician) use generalized additive models on a project that he was involved with. We were pleased with the results of the modelling as they gave us some new insights on his topic and it was very rewardingBecause we were so impressed with the results, we both took the above book out of the library to read. I started the book with some trepid. Seminal work in modern linear models A Buyer Generalized Additive Models (GAMs) represent the modern and computational extension of Generalized Linear Models (GLMs) (see McCullagh and Nelder) which allow for nonlinear effects in the predictor variables. These nonlinear effects are specified nonparametrically through estimation using a scatterplot smoother. GAMs are frequently used in industry (e.g. FICO scores) due to their sp
General concepts are introduced, illustrated with relatively simple applications, and then re-examined at a more theoretical level." -Journal of the Royal Statistical Society, Series C "This book aims to 'provide an up-to-date survey of current research in additive modelling, ' with the 'emphasis on practical rather than theoretical.' With that in mind, the book does extremely well." -Statistics in Medicine "Generalized Additive Models is a good starting point for researchers wishing to initiate themselves in the vast and burgeoning area of large-sample nonparametric model fitting." -Journal of the American Statistical Association "Overall, my impressions of this book are very favorable, and I consider it a book worth owning The material is presented clearly and attractively. "That the authors succeed in conveying their message is largely due to the extremely clear and effective organization of the text. a comple
While McCullagh and Nelder's Generalized Linear Models shows how to extend the usual linear methodology to cover analysis of a range of data types, Generalized Additive Models enhances this methodology even further by incorporating the flexibility of nonparametric regression. Clear prose, exercises in each chapter, and case studies enhance this popular text.. These methods relax the linear assumption of many standard models and allow analysts to uncover structure in the data that might otherwise have been missed. This book describes an array of power tools for data analysis that are based on nonparametric regression and smoothing techniques