The role of corporate governance in environmental policy disclosure and sustainable development. Generalized estimating equations in longitudinal count data analysis.

2020 ◽  
Author(s):  
Francesco Campanella ◽  
Luana Serino ◽  
Anna Crisci ◽  
Antonello D'Ambra
Keyword(s):  
Sustainable Development ◽  
Corporate Governance ◽  
Data Analysis ◽  
Environmental Policy ◽  
Count Data ◽  
Generalized Estimating Equations ◽  
Estimating Equations ◽  
Longitudinal Count Data ◽  
Generalized Estimating

scholarly journals Marginal models for longitudinal count data with dropouts

2020 ◽  
Vol 54 (1) ◽  
pp. 27-42
Author(s):  
Seema Zubair ◽  
Sanjoy K. Sinha
Keyword(s):  
Count Data ◽  
Generalized Estimating Equations ◽  
Linear Models ◽  
Estimating Equations ◽  
Finite Sample ◽  
Marginal Models ◽  
Missing Responses ◽  
Longitudinal Count Data ◽  
Log Linear ◽  
Generalized Estimating

In this article, we investigate marginal models for analyzing incomplete longitudinal count data with dropouts. Specifically, we explore commonly used generalized estimating equations and weighted generalized estimating equations for fitting log-linear models to count data in the presence of monotone missing responses. A series of simulations were carried out to examine the finite-sample properties of the estimators in the presence of both correctly specified and misspecified dropout mechanisms. An application is provided using actual longitudinal survey data from the Health and Retirement Study (HRS) (HRS, 2019)

scholarly journals Second-order generalized estimating equations for correlated count data

2015 ◽  
Vol 31 (2) ◽  
pp. 749-770
Author(s):  
George Kalema ◽  
Geert Molenberghs ◽  
Wondwosen Kassahun
Keyword(s):  
Count Data ◽  
Generalized Estimating Equations ◽  
Estimating Equations ◽  
Second Order ◽  
Generalized Estimating
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