Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies
暫譯: 數據科學中的目標學習：複雜縱向研究的因果推斷

Van Der Laan, Mark J., Rose, Sherri

Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies
  • 出版商: Springer
  • 出版日期: 2018-12-15
  • 售價: $4,560
  • 貴賓價: 9.5 折 $4,332
  • 語言: 英文
  • 頁數: 640
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 3030097366
  • ISBN-13: 9783030097363
  • 相關分類: Machine Learning

    • 海外代購書籍(需單獨結帳)

商品描述

Provides essential data analysis tools for answering complex big data questions based on real world data

Contains machine learning estimators that provide inference within data science

Offers applications that demonstrate 1) the translation of the real world application into a statistical estimation problem and 2) the targeted statistical learning methodology to answer scientific questions of interest based on real data

商品描述(中文翻譯)

提供基本的數據分析工具，以回答基於現實世界數據的複雜大數據問題。

包含機器學習估計器，提供數據科學中的推斷。

提供應用示例，展示 1) 將現實世界應用轉化為統計估計問題，以及 2) 針對性統計學習方法，以根據真實數據回答感興趣的科學問題。

作者簡介

Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. His applied research involves applications in HIV and safety analysis, among others. He has published over 250 journal articles, 4 books, and one handbook on big data. Dr. van der Laan is also co-founder and co-editor of the International Journal of Biostatistics and the Journal of Causal Inference and associate editor of a variety of journals. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics or statistics.

Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose's methodological research focuses on nonparametric machine learning for causal inference and prediction. She has made major contributions to the development and application of targeted learning estimators, as well as adaptations to super learning for varied scientific problems. Within health policy, Dr. Rose works on comparative effectiveness research, health program impact evaluation, and computational health economics. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics.

作者簡介(中文翻譯)

馬克·范德蘭 (Mark van der Laan), PhD, 是加州大學伯克利分校的吉安-平·許/卡爾·E·皮斯 (Jiann-Ping Hsu/Karl E. Peace) 生物統計學與統計學教授。他的研究興趣包括基因組學中的統計方法、生存分析、截尾數據、機器學習、半參數模型、因果推斷和目標學習。他的應用研究涉及HIV和安全性分析等應用。他已發表超過250篇期刊文章、4本書籍和一本關於大數據的手冊。范德蘭博士也是《國際生物統計學期刊》(International Journal of Biostatistics) 和《因果推斷期刊》(Journal of Causal Inference) 的共同創辦人和共同編輯，並擔任多個期刊的副編輯。范德蘭博士於2004年獲得莫提默·斯皮格曼獎 (Mortimer Spiegelman Award)、2005年獲得范丹齊格獎 (Van Dantzig Award)、2005年獲得COPSS斯內德科獎 (COPSS Snedecor Award)、2005年獲得COPSS總統獎 (COPSS Presidential Award)，並指導超過40名生物統計學或統計學的博士生畢業。

雪莉·羅斯 (Sherri Rose), PhD, 是哈佛醫學院的健康政策 (生物統計學) 副教授。她的工作重點在於開發和整合創新的統計方法以促進人類健康。羅斯博士的方法論研究專注於因果推斷和預測的非參數機器學習。她對目標學習估計量的開發和應用做出了重大貢獻，並對超學習在各種科學問題中的適應進行了研究。在健康政策方面，羅斯博士從事比較效果研究、健康計劃影響評估和計算健康經濟學的工作。她共同領導健康政策數據科學實驗室，並目前擔任《美國統計協會期刊》(Journal of the American Statistical Association) 和《生物統計學》(Biostatistics) 的副編輯。

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