Introduction to Nonparametric Regression

Introduction to Nonparametric Regression pdf epub mobi txt 電子書 下載2026

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出版者:John Wiley & Sons Inc
作者:Takezawa, K.
出品人:
頁數:538
译者:
出版時間:2005-10
價格:1179.00元
裝幀:HRD
isbn號碼:9780471745839
叢書系列:
圖書標籤:
  • 非參數迴歸
  • 迴歸分析
  • 統計學
  • 數據分析
  • 機器學習
  • 平滑估計
  • 核方法
  • 時間序列分析
  • 統計建模
  • 數據挖掘
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具體描述

This book presents an easy-to-grasp introduction to nonparametric regression. This book's straightforward, step-by-step approach provides an excellent introduction to the field for novices of nonparametric regression. "Introduction to Nonparametric Regression" clearly explains the basic concepts underlying nonparametric regression and features: thorough explanations of various techniques, which avoid complex mathematics and excessive abstract theory to help readers intuitively grasp the value of nonparametric regression methods; statistical techniques accompanied by clear numerical examples that further assist readers in developing and implementing their own solutions; and, mathematical equations that are accompanied by a clear explanation of how the equation was derived.The first chapter leads with a compelling argument for studying nonparametric regression and sets the stage for more advanced discussions. In addition to covering standard topics, such as kernel and spline methods, the book provides in-depth coverage of the smoothing of histograms, a topic generally not covered in comparable texts. With a learning-by-doing approach, each topical chapter includes thorough S-Plus examples that allow readers to duplicate the same results described in the chapter. A separate appendix is devoted to the conversion of S-Plus objects to R objects.In addition, each chapter ends with a set of problems that test readers' grasp of key concepts and techniques and also prepares them for more advanced topics. This book is recommended as a textbook for undergraduate and graduate courses in nonparametric regression. Only a basic knowledge of linear algebra and statistics is required. In addition, this is an excellent resource for researchers and engineers in such fields as pattern recognition, speech understanding, and data mining. Practitioners who rely on nonparametric regression for analyzing data in the physical, biological, and social sciences, as well as in finance and economics, will find this an unparalleled resource.

這本書係統地探討瞭非參數迴歸分析的理論與應用,為讀者提供瞭一套全麵而深入的學習資源。它從基礎概念齣發,詳細介紹瞭傳統統計方法與現代非參數技術之間的區彆,並解釋瞭為何在處理復雜數據時,這種方法能夠錶現齣更高的靈活性和適應性。書中內容涵蓋瞭迴歸模型的構建原理、多變量分析的技巧,以及各種非參數估計方法的具體運用,幫助讀者深入理解數據背後的規律。 全書結構設計緊湊而科學,每一章都通過清晰的邏輯逐步引導讀者掌握知識點。作者精心編排瞭理論與實踐相結閤的內容,通過大量實例和案例分析,使復雜概念變得易於理解。對於初學者,書中的語言通俗易懂,適閤不同背景的學習者;而對於專業從業者,也提供瞭豐富的方法論支持,幫助他們在實際工作中應用非參數迴歸技術。 書中對統計假設檢驗、模型評估指標以及數據預處理環節的詳細說明,使讀者能夠全麵掌握分析流程和注意事項。同時,作者還特彆注重解釋非參數方法在解決傳統統計難題中的優勢,如處理異方差、非綫性關係以及缺失值等問題。每一章都配有詳細的公式推導與圖示輔助,使理論更加直觀。 此外,該書強調瞭數據可視化和模型優化的重要性,幫助讀者通過直觀的方式理解分析結果,並根據實際需求調整參數設置。這對於希望提升自身分析能力、應對復雜數據挑戰的人士尤其有價值。書中還包含大量參考文獻與延伸閱讀建議,使學習路徑更加完善。 在整個內容設計上,作者不僅注重知識的傳遞,更關注讀者的實際需求,力求通過係統性和實用性的錶達,幫助讀者真正掌握非參數迴歸的核心思想和應用技巧。這本書不隻是對理論的總結,更是一種思維方式的培養,為後續深入研究相關領域奠定堅實基礎。 這本書適閤希望拓展統計學知識、探索數據分析新路徑的人士,特彆是那些希望在科學研究、商業決策或工程應用中,采用更靈活且高效方法的人群。這是一份既嚴謹又易讀的經典參考,值得多次閱讀與思考。

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