Survival Analysis
暫譯: 生存分析
Vaman, H. J., Tattar, Prabhanjan
- 出版商: CRC
- 出版日期: 2022-08-26
- 售價: $4,910
- 貴賓價: 9.5 折 $4,665
- 語言: 英文
- 頁數: 284
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 0367030373
- ISBN-13: 9780367030377
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商品描述
Survival analysis generally deals with analysis of data arising from clinical trials. Censoring, truncation, and missing data create analytical challenges and the statistical methods and inference require novel and different approaches for analysis. Statistical properties, essentially asymptotic ones, of the estimators and tests are aptly handled in the counting process framework which is drawn from the larger arm of stochastic calculus. With explosion of data generation during the past two decades, survival data has also enlarged assuming a gigantic size. Most statistical methods developed before the millennium were based on a linear approach even in the face of complex nature of survival data. Nonparametric nonlinear methods are best envisaged in the Machine Learning school. This book attempts to cover all these aspects in a concise way.
Survival Analysis offers an integrated blend of statistical methods and machine learning useful in analysis of survival data. The purpose of the offering is to give an exposure to the machine learning trends for lifetime data analysis.
Features:
- Classical survival analysis techniques for estimating statistical functional and hypotheses testing
- Regression methods covering the popular Cox relative risk regression model, Aalen's additive hazards model, etc.
- Information criteria to facilitate model selection including Akaike, Bayes, and Focused
- Penalized methods consisting of, and elastic net
- Survival trees and ensemble techniques of bagging, boosting, and random survival forests
- A brief exposure of neural networks for survival data
- R program illustration throughout the book
商品描述(中文翻譯)
生存分析 通常處理來自臨床試驗的數據分析。截尾、截斷和缺失數據帶來了分析挑戰，統計方法和推斷需要新穎且不同的分析方法。估計量和檢驗的統計性質，基本上是漸近性質，適當地在計數過程框架中處理，這是從隨機微積分的更大範疇中引出的。隨著過去二十年數據生成的爆炸性增長，生存數據的規模也隨之擴大。大多數在千禧年之前開發的統計方法，即使面對生存數據的複雜性，仍然基於線性方法。非參數非線性方法在機器學習領域中最為理想。本書試圖以簡潔的方式涵蓋所有這些方面。
生存分析提供了統計方法和機器學習的綜合融合，對生存數據的分析非常有用。本書的目的是讓讀者接觸到針對生命數據分析的機器學習趨勢。
特色：
- 經典生存分析技術，用於估計統計函數和假設檢驗
- 回歸方法，包括流行的Cox相對風險回歸模型、Aalen的加性危險模型等
- 信息準則以促進模型選擇，包括Akaike、Bayes和Focused
- 懲罰方法，包括彈性網
- 生存樹和集成技術，如袋裝、提升和隨機生存森林
- 對生存數據的神經網絡簡要介紹
- 全書中包含R程式示例
作者簡介
Prabhanjan Narayanachar Tattar is working as a Lead Data Scientist at British American Tobacco company, Malaysia. The author has published several books in Statistics: A Course in Statistics with R (Wiley), Statistical Application Development with R and Python, and Hands-on Ensemble Learning with R. He is recipient of the IBS(IR)- GK Shukla Young Biometrician Award (2005) and the Dr. U.S. Nair Award for Young Statistician (2007). He held SRF of CSIR-UGC during PhD. In the year 2021, he has ventured into ction writing and published three novels under the penname of S.B. Akshobhya: The Panipuri Crimes, Finding - A Measure of Her, and Prema Naada Pandita.
H. J. Vaman is a retired professor of Statistics. He taught for over 40 years at Bangaore University and Central University of Rajasthan. He has also served as visiting faculty at Shivaji University, University of Calcutta, Indian Statistical Institute, Bangalore Centre, IIT-Mumbai, and Mangalore University. His main areas of research are sequential decision processes, survival analysis, statistical process control, and modelling in certain health related studies.
作者簡介(中文翻譯)
Prabhanjan Narayanachar Tattar 目前擔任馬來西亞英美煙草公司的首席數據科學家。作者已出版多本統計學書籍，包括《使用 R 的統計學課程》（Wiley）、《使用 R 和 Python 的統計應用開發》以及《使用 R 的實作集成學習》。他曾獲得 IBS(IR)-GK Shukla 年輕生物統計學家獎（2005年）和 Dr. U.S. Nair 年輕統計學家獎（2007年）。在攻讀博士學位期間，他曾擔任 CSIR-UGC 的 SRF。2021年，他開始從事小說寫作，並以筆名 S.B. Akshobhya 發表了三部小說：《潘尼普里犯罪》、《尋找 - 她的度量》和《愛的旋律》。
H. J. Vaman 是一位退休的統計學教授。他在班加羅爾大學和拉賈斯坦邦中央大學教授超過 40 年。他還曾擔任西瓦吉大學、加爾各答大學、印度統計學研究所班加羅爾中心、印度理工學院孟買分校和曼加羅爾大學的訪問教員。他的主要研究領域包括序列決策過程、生存分析、統計過程控制以及某些健康相關研究中的建模。
