This book describes an array of power tools for data analysis that are based on nonparametric regression and smoothing techniques. 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. 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.
具體描述
讀後感
用戶評價
I guess I won't touch GAM again in the future. The advanced ML project I did with this framework was quite uneasy. But, still want to pay tribute to this elegant creation. It encompasses so much potential for non-parametric methods. Yet it's just so hard...
评分大師早期作品，竟然評論聊聊。感謝大師對cox模型推導，幫助我更好的理解瞭cox模型似然形式，感恩。
评分I guess I won't touch GAM again in the future. The advanced ML project I did with this framework was quite uneasy. But, still want to pay tribute to this elegant creation. It encompasses so much potential for non-parametric methods. Yet it's just so hard...
评分大師早期作品，竟然評論聊聊。感謝大師對cox模型推導，幫助我更好的理解瞭cox模型似然形式，感恩。
评分ar{y}=Sy，這個等式是如何成立的？貌似不看這本書也應該想到為什麼成立。
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