| 书目名称 | Targeted Learning in Data Science |
| 副标题 | Causal Inference for |
| 编辑 | Mark J. van der Laan,Sherri Rose |
| 视频video | |
| 概述 | 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 applicat |
| 丛书名称 | Springer Series in Statistics |
| 图书封面 |  |
| 描述 | .This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in .Targeted Learning in Data .Science. are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, .Targeted Learning., published in 2011..Mark van der Laan |
| 出版日期 | Textbook 2018 |
| 关键词 | targeted minimum loss estimation; targeted learning; longitudinal data; big data; precision medicine; tar |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-65304-4 |
| isbn_softcover | 978-3-030-09736-3 |
| isbn_ebook | 978-3-319-65304-4Series ISSN 0172-7397 Series E-ISSN 2197-568X |
| issn_series | 0172-7397 |
| copyright | Springer International Publishing AG, part of Springer Nature 2018 |
发表于 2025-3-21 17:54:57
