Nonparametric Inference
暫譯: 非參數推斷
Koul, Hira L., Schick, Anton, Vellaisamy, Palaniappan
- 出版商: CRC
- 出版日期: 2026-08-06
- 售價: $4,340
- 貴賓價: 9.5 折 $4,123
- 語言: 英文
- 頁數: 354
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 1032956135
- ISBN-13: 9781032956138
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相關分類:
機率統計學 Probability-and-statistics
尚未上市，無法訂購
商品描述
This book provides a comprehensive and balanced treatment of both classical and modern methods in nonparametric inference. It begins with foundational topics such as order statistics, ranks, and confidence intervals for medians and percentiles before progressing to distribution-free tests, robust estimators, regression quantiles and U-statistics. Advanced topics include nonparametric density and regression estimation, model diagnostics, empirical likelihood, and survival analysis, including nonparametric Bayesian and maximum likelihood estimators. The book uniquely integrates these topics into a single resource, making it distinct from other texts in the field.
Key Features:
- A balanced blend of classical methods (e.g., rank and sign tests) and modern techniques (e.g., bootstrap, empirical likelihood, and nonparametric regression).
- Comprehensive coverage of nonparametric density and regression estimation, model diagnostics, and survival analysis, including Bayesian and maximum likelihood approaches.
- Unique inclusion of empirical likelihood inference, a broadly applicable and essential methodology for contemporary graduate courses.
- Numerous exercises and notes at the end of chapters to reinforce concepts and provide historical context.
- Designed for both teaching and reference, offering up-to-date techniques in nonparametric inference.
This text is ideal for a two-semester course on nonparametric inference for graduate students in statistics, applied mathematics, machine learning, and computer science. It also serves as a valuable reference for researchers and practitioners interested in nonparametric methods. Its comprehensive scope, including empirical likelihood, nonparametric Bayes, and bootstrap methodologies, makes it a unique resource. Notes at the end of each chapter provide insights into the chronological development of the field, while numerous exercises help reinforce the concepts and methodologies presented.
商品描述(中文翻譯)
這本書全面且平衡地探討了非參數推斷中的經典與現代方法。它從基礎主題開始，例如順序統計、排名以及中位數和百分位數的信賴區間，然後進一步探討無分佈假設的檢定、穩健估計量、回歸分位數和 U 統計量。進階主題包括非參數密度和回歸估計、模型診斷、經驗似然以及生存分析，包括非參數貝葉斯和最大似然估計量。這本書獨特地將這些主題整合成一個單一資源，使其在該領域的其他文本中脫穎而出。
**主要特點：**
- 經典方法（例如排名和符號檢定）與現代技術（例如自助法、經驗似然和非參數回歸）的平衡結合。
- 全面涵蓋非參數密度和回歸估計、模型診斷和生存分析，包括貝葉斯和最大似然方法。
- 獨特地納入經驗似然推斷，這是一種廣泛適用且對當代研究生課程至關重要的方法論。
- 每章末尾有大量練習和註解，以加強概念並提供歷史背景。
- 設計用於教學和參考，提供最新的非參數推斷技術。
這本書非常適合用於針對統計、應用數學、機器學習和計算機科學研究生的兩學期非參數推斷課程。它也為對非參數方法感興趣的研究人員和實務工作者提供了寶貴的參考。其全面的範疇，包括經驗似然、非參數貝葉斯和自助法，使其成為一個獨特的資源。每章末尾的註解提供了該領域的時間發展見解，而大量的練習有助於加強所呈現的概念和方法論。
作者簡介
Hira L. Koul secured his doctorate in statistics from the University of California, Berkeley in 1967. He joined the Department of Statistics and Probability, Michigan State University (MSU) on January 1, 1968. Since January 1, 2018, he has been Professor Emeritus at the MSU, after serving there as a faculty member for 50 years. His areas of research include nonparametric inference, inference on short and long memory processes, time series analysis and survival analysis. He has published around 150 papers, several monographs and books and guided 35 doctoral theses. He is a Fellow of the American Statistical Association and of the Institute of Mathematical Statistics and Past President of the International Indian Statistical Association. He was a recipient of a Humboldt Research Award for senior scientists in October 1995 and a Distinguished Faculty Award at the MSU, 2005.
Anton Schick earned his doctorate in statistics from Michigan State University in 1983. He spent one year at Tufts University before joining the Department of Mathematical Sciences at Binghamton University in the fall of 1984. He retired as full professor on September 1, 2024 after forty years of service that included two terms as chair. His research has focused on the characterization and construction of efficient statistical inference procedures in nonparametric and semiparametric models with an emphasis on regression and time series models, on curve estimation with parametric rates, on inference with incomplete data, and on the empirical likelihood approach. He has published one hundred twenty research papers and guided ten doctoral theses.
Palaniappan Vellaisamy is currently a Visiting Professor in the Department of Statistics and Applied Probability, University of California, Santa Barbara, USA. He completed his Ph.D. degree in statistics from the Indian Institute of Technology Kanpur in 1989. He worked as a Research Associate from July 1989 to December 1990 at the Indian Statistical Institute, New Delhi. Then he joined in 1991 as an Assistant Professor in the Department of Mathematics at Indian Institute of Technology Bombay, India. He became a full professor in 2003 and retired in June 2024. His research areas include statistical inference, applied probability, and fractional stochastic processes. He has published more than 120 research papers in various journals of statistics and probability and has guided 11 Ph.D.'s. He is currently an Associate Editor for Statistics and Probability Letters and The Journal of Indian Statistical Association.
作者簡介(中文翻譯)
Hira L. Koul於1967年在加州大學伯克利分校獲得統計學博士學位。他於1968年1月1日加入密西根州立大學（Michigan State University, MSU）統計與機率系。自2018年1月1日起，他成為MSU的名譽教授，並在該校擔任教職長達50年。他的研究領域包括非參數推斷、短期和長期記憶過程的推斷、時間序列分析和生存分析。他已發表約150篇論文，幾本專著和書籍，並指導了35篇博士論文。他是美國統計協會（American Statistical Association）和數學統計學會（Institute of Mathematical Statistics）的會士，以及國際印度統計協會（International Indian Statistical Association）的前任會長。他於1995年10月獲得洪堡研究獎（Humboldt Research Award）以表彰資深科學家，並於2005年獲得MSU的傑出教職員獎（Distinguished Faculty Award）。
Anton Schick於1983年在密西根州立大學獲得統計學博士學位。他在塔夫茨大學（Tufts University）度過了一年，然後於1984年秋季加入賓漢頓大學（Binghamton University）數學科學系。他於2024年9月1日退休，擔任全職教授，並在四十年的服務中擔任過兩屆系主任。他的研究專注於在非參數和半參數模型中有效統計推斷程序的特徵化和構建，特別強調回歸和時間序列模型、具有參數速率的曲線估計、不完整數據的推斷以及經驗似然方法。他已發表120篇研究論文並指導了十篇博士論文。
Palaniappan Vellaisamy目前是美國加州大學聖塔巴巴拉分校（University of California, Santa Barbara）統計與應用機率系的訪問教授。他於1989年在印度理工學院坎普爾分校（Indian Institute of Technology Kanpur）獲得統計學博士學位。1989年7月至1990年12月，他在印度統計學院（Indian Statistical Institute, New Delhi）擔任研究助理。隨後，他於1991年加入印度理工學院孟買分校（Indian Institute of Technology Bombay）數學系擔任助理教授。他於2003年晉升為全職教授，並於2024年6月退休。他的研究領域包括統計推斷、應用機率和分數隨機過程。他在各種統計和機率期刊上發表了超過120篇研究論文，並指導了11篇博士論文。他目前是Statistics and Probability Letters和The Journal of Indian Statistical Association的副編輯。
