Semiparametric Theory and Missing Data 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024


Semiparametric Theory and Missing Data

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Tsiatis, Anastasios A. 作者
Springer-Verlag New York Inc.
译者
2010-2 出版日期
399 页数
$ 90.34 价格
丛书系列
9781441921857 图书编码

Semiparametric Theory and Missing Data 在线电子书 图书标签: Statistics  非参数统计  Semiparametric  MissingData  Mathematics  数学   


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发表于2024-12-28


Semiparametric Theory and Missing Data 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Semiparametric Theory and Missing Data 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Semiparametric Theory and Missing Data 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024



Semiparametric Theory and Missing Data 在线电子书 用户评价

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Very informative discussion.

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fairly nice and straightforward explanation for eff semipar bound in the event that the parameter of interest can be concretely written. It is however relying too much on the restricted moment model to prove things and to some extent, skipping the elegant treatment using generic fuctional analysis methods. Bridging this book to BKRW is hard.

评分

fairly nice and straightforward explanation for eff semipar bound in the event that the parameter of interest can be concretely written. It is however relying too much on the restricted moment model to prove things and to some extent, skipping the elegant treatment using generic fuctional analysis methods. Bridging this book to BKRW is hard.

评分

Very informative discussion.

评分

Very informative discussion.

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Semiparametric Theory and Missing Data 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

Semiparametric Theory and Missing Data 在线电子书 图书描述

This book summarizes current knowledge regarding the theory of estimation for semiparametric models with missing data, in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible.

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