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Longitudinal data analysis using generalized maximum entropy approach
Joint Authors
al-Rawwash, Muhammad Y.
al-Nasir, Amjad D.
Source
Jordan Journal of Mathematics and Statistics
Issue
Vol. 4, Issue 1 (30 Apr. 2011), pp.47-60, 14 p.
Publisher
Yarmouk University Deanship of Research and Graduate Studies
Publication Date
2011-04-30
Country of Publication
Jordan
No. of Pages
14
Main Subjects
Abstract EN
Marginal generalized linear models are frequently used for the analysis of repeated measurements and longitudinal data.
During the last three decades, researchers used parametric, nonparametric as well as Bayesian methods as useful approaches to model such kind of data.
The correlation among the repeated measurements is considered a vital factor to increase the estimation efficiency of the model’s parameters for different correlation structures.
This article suggests using the generalized maximum entropy (GME) as an efficient method for the joint modeling of mean and correlation parameters that permits the estimation with minimum distributional assumptions.
Moreover, we present a simulation study to compare the performance of the GME method with a set of well-known estimation methods in the longitudinal data literatures.
American Psychological Association (APA)
al-Rawwash, Muhammad Y.& al-Nasir, Amjad D.. 2011. Longitudinal data analysis using generalized maximum entropy approach. Jordan Journal of Mathematics and Statistics،Vol. 4, no. 1, pp.47-60.
https://search.emarefa.net/detail/BIM-266406
Modern Language Association (MLA)
al-Rawwash, Muhammad Y.& al-Nasir, Amjad D.. Longitudinal data analysis using generalized maximum entropy approach. Jordan Journal of Mathematics and Statistics Vol. 4, no. 1 (Apr. 2011), pp.47-60.
https://search.emarefa.net/detail/BIM-266406
American Medical Association (AMA)
al-Rawwash, Muhammad Y.& al-Nasir, Amjad D.. Longitudinal data analysis using generalized maximum entropy approach. Jordan Journal of Mathematics and Statistics. 2011. Vol. 4, no. 1, pp.47-60.
https://search.emarefa.net/detail/BIM-266406
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 58-60
Record ID
BIM-266406