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Titlebook: Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates; Jeffrey R. Wilson,Elsa Vazquez-Arreola,(Din) Ding- B

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書目名稱Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates
編輯Jeffrey R. Wilson,Elsa Vazquez-Arreola,(Din) Ding-
視頻videohttp://file.papertrans.cn/624/623871/623871.mp4
概述Features pioneering developments of computational and methodological statistics and biostatistics with applications to real and familiar datasets.Presents affiliated data and computer programs so that
叢書名稱Emerging Topics in Statistics and Biostatistics
圖書封面Titlebook: Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates;  Jeffrey R. Wilson,Elsa Vazquez-Arreola,(Din) Ding- B
描述.This monograph provides a concise point of research topics and reference for modeling correlated response data with time-dependent covariates, and longitudinal data for the analysis of population-averaged models, highlighting methods by a variety of pioneering scholars. While the models presented in the volume are applied to health and health-related data, they can be used to analyze any kind of data that contain covariates that change over time. The included data are analyzed with the use of both R and SAS, and the data and computing programs are provided to readers so that they can replicate and implement covered methods. It is an excellent resource for scholars of both computational and methodological statistics and biostatistics, particularly in the applied areas of health. ?.
出版日期Book 2020
關(guān)鍵詞generalized method of moments estimators; GMM estimators; method of moments; estimators; GMM; correlated
版次1
doihttps://doi.org/10.1007/978-3-030-48904-5
isbn_softcover978-3-030-48906-9
isbn_ebook978-3-030-48904-5Series ISSN 2524-7735 Series E-ISSN 2524-7743
issn_series 2524-7735
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Simultaneous Modeling with Time-Dependent Covariates and Bayesian Intervals,likelihood function of the simultaneous responses is impossible to afford maximum likelihood estimates. Thus a simultaneous modeling of responses with a working correlation matrix to reflect the hierarchical aspect are presented. Bayesian intervals based on the partitioning of the data matrix is obtained. A demonstration of a fit of a model to ..
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