Nonparametric Models for Longitudinal Data: With Implementation in R
暫譯: 長期資料的非參數模型：R 語言實作

Wu, Colin O., Tian, Xin

Nonparametric Models for Longitudinal Data: With Implementation in R
  • 出版商: CRC
  • 出版日期: 2020-06-30
  • 售價: $2,470
  • 貴賓價: 9.5 折 $2,347
  • 語言: 英文
  • 頁數: 582
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 0367571668
  • ISBN-13: 9780367571665

    • 海外代購書籍(需單獨結帳)

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商品描述

Nonparametric Models for Longitudinal Data with Implementations in R presents a comprehensive summary of major advances in nonparametric models and smoothing methods with longitudinal data. It covers methods, theories, and applications that are particularly useful for biomedical studies in the era of big data and precision medicine. It also provides flexible tools to describe the temporal trends, covariate effects and correlation structures of repeated measurements in longitudinal data.

 

 

This book is intended for graduate students in statistics, data scientists and statisticians in biomedical sciences and public health. As experts in this area, the authors present extensive materials that are balanced between theoretical and practical topics. The statistical applications in real-life examples lead into meaningful interpretations and inferences.

 

 

 

 

Features:

 

 

 

 

  • Provides an overview of parametric and semiparametric methods
  • Shows smoothing methods for unstructured nonparametric models
  • Covers structured nonparametric models with time-varying coefficients
  • Discusses nonparametric shared-parameter and mixed-effects models
  • Presents nonparametric models for conditional distributions and functionals
  • Illustrates implementations using R software packages
  • Includes datasets and code in the authors' website
  • Contains asymptotic results and theoretical derivations

 

 

 

 

Both authors are mathematical statisticians at the National Institutes of Health (NIH) and have published extensively in statistical and biomedical journals. Colin O. Wu earned his Ph.D. in statistics from the University of California, Berkeley (1990), and is also Adjunct Professor at the Georgetown University School of Medicine. He served as Associate Editor for Biometrics and Statistics in Medicine, and reviewer for National Science Foundation, NIH, and the U.S. Department of Veterans Affairs. Xin Tian earned her Ph.D. in statistics from Rutgers, the State University of New Jersey (2003). She has served on various NIH committees and collaborated extensively with clinical researchers.

 

 

商品描述(中文翻譯)

《長期資料的非參數模型 及其在 R 中的實作》提供了非參數模型和平滑方法在長期資料方面的主要進展的綜合總結。它涵蓋了在大數據和精準醫療時代中，對生物醫學研究特別有用的方法、理論和應用。該書還提供了靈活的工具，以描述長期資料中重複測量的時間趨勢、協變數效應和相關結構。

本書旨在為統計學研究生、生物醫學科學和公共衛生領域的數據科學家及統計學家提供參考。作為該領域的專家，作者呈現了理論與實務主題之間平衡的廣泛材料。真實案例中的統計應用引導出有意義的解釋和推論。

特色：

- 提供參數和半參數方法的概述
- 展示無結構非參數模型的平滑方法
- 涵蓋具有時間變化係數的結構性非參數模型
- 討論非參數共享參數和混合效應模型
- 提出條件分佈和函數的非參數模型
- 使用 R 軟體包進行實作示範
- 包含作者網站上的數據集和程式碼
- 包含漸近結果和理論推導

兩位作者均為國立衛生研究院 (NIH) 的數學統計學家，並在統計和生物醫學期刊上發表了大量研究。Colin O. Wu 於加州大學伯克利分校獲得統計學博士學位 (1990)，並且是喬治城大學醫學院的兼任教授。他曾擔任《生物統計學》和《醫學統計》期刊的副編輯，並擔任美國國家科學基金會、NIH 和美國退伍軍人事務部的審稿人。Xin Tian 於新澤西州立大學拉德格斯校區獲得統計學博士學位 (2003)。她曾在多個 NIH 委員會任職，並與臨床研究人員廣泛合作。

作者簡介

Both authors are mathematical statisticians at the National Institutes of Health (NIH) and have published extensively in statistical and biomedical journals. Colin O. Wu earned his Ph.D. in statistics from the University of California, Berkeley (1990), and is also Adjunct Professor at the Georgetown University School of Medicine. He served as Associate Editor for Biometrics and Statistics in Medicine, and reviewer for National Science Foundation, NIH, and the U.S. Department of Veterans Affairs. Xin Tian earned her Ph.D. in statistics from Rutgers, the State University of New Jersey (2003). She has served on various NIH committees and collaborated extensively with clinical researchers.

作者簡介(中文翻譯)

兩位作者均為國立衛生研究院（NIH）的數學統計學家，並在統計及生物醫學期刊上發表了大量研究。Colin O. Wu 於加州大學伯克利分校獲得統計學博士學位（1990年），並且是喬治城大學醫學院的兼任教授。他曾擔任Biometrics和Statistics in Medicine的副編輯，並擔任美國國家科學基金會、NIH及美國退伍軍人事務部的審稿人。Xin Tian 於新澤西州立大學羅格斯分校獲得統計學博士學位（2003年）。她曾參與多個NIH委員會，並與臨床研究人員進行廣泛合作。

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