| 书目名称 | Semiparametric Theory and Missing Data |
| 编辑 | Anastasios A. Tsiatis |
| 视频video | |
| 概述 | Unifies the two approaches to the topic of missing data |
| 丛书名称 | Springer Series in Statistics |
| 图书封面 |  |
| 描述 | .Missing data arise in almost all scientific disciplines. In many cases, the treatment of missing data in an analysis is carried out in a casual and ad-hoc manner, leading, in many cases, to invalid inference and erroneous conclusions. In the past 20 years or so, there has been a serious attempt to understand the underlying issues and difficulties that come about from missing data and their impact on subsequent analysis. There has been a great deal written on the theory developed for analyzing missing data for finite-dimensional parametric models. This includes an extensive literature on likelihood-based methods and multiple imputation. More recently, there has been increasing interest in semiparametric models which, roughly speaking, are models that include both a parametric and nonparametric component. Such models are popular because estimators in such models are more robust than in traditional parametric models. The theory of missing data applied to semiparametric models is scattered throughout the literature with no thorough comprehensive treatment of the subject....This book combines much of what is known in regard to the theory of estimation for semiparametric models with mis |
| 出版日期 | Book 2006 |
| 关键词 | average; estimator; likelihood; probability; semiparametric |
| 版次 | 1 |
| doi | https://doi.org/10.1007/0-387-37345-4 |
| isbn_softcover | 978-1-4419-2185-7 |
| isbn_ebook | 978-0-387-37345-4Series ISSN 0172-7397 Series E-ISSN 2197-568X |
| issn_series | 0172-7397 |
| copyright | Springer-Verlag New York 2006 |
发表于 2025-3-21 17:32:20
