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Marginal models in analysis of corre...

Chen, (Din) Ding-Geng.

Marginal models in analysis of correlated binary data with time dependent covariates

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Marginal models in analysis of correlated binary data with time dependent covariatesby Jeffrey R. Wilson, Elsa Vazquez-Arreola, (Din) Ding-Geng Chen.
作者:	Wilson, Jeffrey R.
其他作者:	Vazquez-Arreola, Elsa.
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	xxiii, 166 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Analysis of covariance.
電子資源:	https://doi.org/10.1007/978-3-030-48904-5
ISBN:	9783030489045$q(electronic bk.)

Marginal models in analysis of correlated binary data with time dependent covariates
Wilson, Jeffrey R.

Marginal models in analysis of correlated binary data with time dependent covariates[electronic resource] /by Jeffrey R. Wilson, Elsa Vazquez-Arreola, (Din) Ding-Geng Chen. - Cham :Springer International Publishing :2020. - xxiii, 166 p. :ill., digital ;24 cm. - Emerging topics in statistics and biostatistics,2524-7735. - Emerging topics in statistics and biostatistics..

1. Introduction to Binary Regression Models -- 2. Generalized Estimating Equations Binary Models -- 3. Lai and Small Models for Time-Dependent Covariates -- 4. Lalonde, wilson, and Yin Models for Time-Dependent Covariates -- 5. Irimata, Broatch, and Wilson Models for Time-Dependent Covariates -- 6. Bayesian GMM to IBW Method of Analysis -- 7. Models for Joint Responses for Time-Dependent Covariates -- 8. Other Models for Time-Dependent Covariates.

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.

ISBN: 9783030489045$q(electronic bk.)

Standard No.: 10.1007/978-3-030-48904-5doiSubjects--Topical Terms:

273447
Analysis of covariance.

LC Class. No.: QA401 / .W55 2020

Dewey Class. No.: 519.538

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