| 書目名稱 | Unified Methods for Censored Longitudinal Data and Causality |
| 編輯 | Mark J. Laan,James M. Robins |
| 視頻video | http://file.papertrans.cn/943/942020/942020.mp4 |
| 概述 | Includes supplementary material: |
| 叢書名稱 | Springer Series in Statistics |
| 圖書封面 |  |
| 描述 | During the last decades, there has been an explosion in computation and information technology. This development comes with an expansion of complex observational studies and clinical trials in a variety of fields such as medicine, biology, epidemiology, sociology, and economics among many others, which involve collection of large amounts of data on subjects or organisms over time. The goal of such studies can be formulated as estimation of a finite dimensional parameter of the population distribution corresponding to the observed time- dependent process. Such estimation problems arise in survival analysis, causal inference and regression analysis. This book provides a fundamental statistical framework for the analysis of complex longitudinal data. It provides the first comprehensive description of optimal estimation techniques based on time-dependent data structures subject to informative censoring and treatment assignment in so called semiparametric models. Semiparametric models are particularly attractive since they allow the presence of large unmodeled nuisance parameters. These techniques include estimation of regression parameters in the familiar (multivariate) generalized lin |
| 出版日期 | Book 2003 |
| 關(guān)鍵詞 | Censoring; Computerassistierte Detektion; Estimator; Maxima; Radiologieinformationssystem; Regression ana |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-0-387-21700-0 |
| isbn_softcover | 978-1-4419-3055-2 |
| isbn_ebook | 978-0-387-21700-0Series ISSN 0172-7397 Series E-ISSN 2197-568X |
| issn_series | 0172-7397 |
| copyright | Springer Science+Business Media New York 2003 |
|Archiver|手機版|小黑屋|
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