Review of Generalized Linear Models and Generalized Estimating Equations

2014 ◽  
pp. 35-58
Keyword(s):  
Generalized Linear Models ◽  
Generalized Estimating Equations ◽  
Linear Models ◽  
Estimating Equations ◽  
Generalized Estimating

Generalized Estimating Equations

2010 ◽  
Vol 49 (05) ◽  
pp. 421-425 ◽  
Author(s):  
M. Vens ◽  
A. Ziegler
Keyword(s):  
Correlation Matrix ◽  
Generalized Estimating Equations ◽  
Linear Models ◽  
Estimating Equations ◽  
Optimal Choice ◽  
Range Restriction ◽  
Dependent Variables ◽  
Mean Structure ◽  
Working Correlation ◽  
Generalized Estimating

Summary Background: Generalized estimating equations (GEE) are an extension of generalized linear models (GLM) in that they allow adjusting for correlations between observations. A major strength of GEE is that they do not require the correct specification of the multivariate distribution but only of the mean structure. Objectives: Several concerns have been raised about the validity of GEE when applied to dichotomous dependent variables. In this contribution, we summarize the theoretical findings concerning efficiency and validity of GEE. Methods: We introduce the GEE in a formal way, summarize general findings on the choice of the working correlation matrix, and show the existence of a dilemma for the optimal choice of the working correlation matrix for dichotomous dependent variables. Results: Biological and statistical arguments for choosing a specific working correlation matrix are given. Three approaches are described for overcoming the range restriction of the correlation coefficient. Conclusions: The three approaches described in this article for overcoming the range restrictions for dichotomous dependent variables in GEE models provide a simple and practical way for use in applications.

scholarly journals Some Generalized Estimating Equations Models Based on Causality Tests for Investigation of The Economic Growth of The Country Groups

2021 ◽  
Vol 46 (3) ◽  
pp. 297-315
Author(s):  
Harun Yonar ◽  
Neslihan İyit
Keyword(s):  
Economic Growth ◽  
Generalized Estimating Equations ◽  
Energy Use ◽  
Linear Models ◽  
Estimating Equations ◽  
Causality Test ◽  
Goods And Services ◽  
Income Groups ◽  
Per Capita ◽  
Generalized Estimating

Abstract In this study, investigation of the economic growth of the Organization for Economic Cooperation and Development (OECD) countries and the countries in different income groups in the World Data Bank is conducted by using causality analyses and Generalized Estimating Equations (GEEs) which is an extension of Generalized Linear Models (GLMs). Eight different macro-economic, energy and environmental variables such as the gross domestic product (GDP) (current US$), CO2 emission (metric tons per capita), electric power consumption (kWh per capita), energy use (kg of oil equivalent per capita), imports of goods and services (% of GDP), exports of goods and services (% of GDP), foreign direct investment (FDI) and population growth rate (annual %) have been used. These countries have been categorized according to their OECD memberships and income groups. The causes of the economic growth of these countries belonging to their OECD memberships and income groups have been determined by using the Toda-Yamamoto causality test. Furthermore, various GEE models have been established for the economic growth of these countries belonging to their OECD membership and income groups in the aspect of the above variables. These various GEE models for the investigation of the economic growth of these countries have been compared to examine the contribution of the causality analyses to the statistical model establishment. As a result of this study, the highlight is found as the use of causally-related variables in the causality-based GEE models is much more appropriate than in the non-causality based GEE models for determining the economic growth profiles of these countries.

Discussion of “Generalized Estimating Equations: Notes on the Choice of the Working Correlation Matrix”

2010 ◽  
Vol 49 (05) ◽  
pp. 426-432 ◽  
Author(s):  
J. Breitung ◽  
N. R. Chaganty ◽  
R. M. Daniel ◽  
M. G. Kenward ◽  
M. Lechner ◽  
...  
Keyword(s):  
Correlation Matrix ◽  
Generalized Estimating Equations ◽  
Linear Models ◽  
Generalized Method Of Moments ◽  
Estimating Equations ◽  
Missing At Random ◽  
Careful Analysis ◽  
Estimation Efficiency ◽  
Working Correlation ◽  
Generalized Estimating

Summary Objective: To discuss generalized estimating equations as an extension of generalized linear models by commenting on the paper of Ziegler and Vens “Generalized Estimating Equations: Notes on the Choice of the Working Correlation Matrix”. Methods: Inviting an international group of experts to comment on this paper. Results: Several perspectives have been taken by the discussants. Econometricians have established parallels to the generalized method of moments (GMM). Statisticians discussed model assumptions and the aspect of missing data. Applied statisticians commented on practical aspects in data analysis. Conclusions: In general, careful modeling correlation is encouraged when considering estimation efficiency and other implications, and a comparison of choosing instruments in GMM and generalized estimating equations (GEE) would be worthwhile. Some theoretical drawbacks of GEE need to be further addressed and require careful analysis of data. This particularly applies to the situation when data are missing at random.

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 Change in Knowledge of Kindergarten Employees Participating in the Course “Diet full of life” in the Field of Children’s Nutrition, as Assessed by Generalized Estimating Equations

2016 ◽  
Vol 47 (1) ◽  
pp. 113-128 ◽  
Author(s):  
Magdalena Zalewska ◽  
Jacek Jamiołkowski ◽  
Agnieszka Genowska ◽  
Irena Białokoz-Kalinowska ◽  
Anna Daszuta-Zalewska ◽  
...  
Keyword(s):  
Higher Education ◽  
Generalized Estimating Equations ◽  
Linear Models ◽  
Eating Habits ◽  
Age Groups ◽  
Estimating Equations ◽  
Supplemental Education ◽  
Children's Nutrition ◽  
The Impact ◽  
Generalized Estimating

Abstract Nutrition is one of the most important environmental factors affecting the physical development and health of children. Education in this area and the development of proper eating habits are priorities. A prerequisite for the proper nutrition of preschool children is knowledge of proper nutrition of people working there. The aim of this study was an evaluation of the knowledge of kindergarten employees participating in the course “Diet full of life – courses in the field of children’s nutrition”. The study included 90 employees of nurseries and kindergartens, participants of the course in the field of children’s nutrition. The research tool was an original questionnaire. Study I (pre-test) was performed before the beginning of the course, while study II (post-test) was performed after its completion. Generalized Linear Models with a Generalized Estimating Equations extension was used to estimate the impact of the number of covariates on knowledge of course participants, taking into consideration the correlation between before- and after-course results. An increase in the knowledge of the participants of the investigated course on children’s nutritional standards was significant and reached 2.053 points on average. No relationship between age, job position, and knowledge level was determined. In the area of principles of proper nutrition for children, older participants had a lower level of knowledge compared to younger ones, and participants with higher education showed a significantly higher knowledge increase as compared to those with vocational education. A significant knowledge increase in the field of dietary behaviors of children was obtained during the course by all examined women, 1.6 points on average (p < 0.001). Younger participants obtained significantly more knowledge from the course than older ones (p < 0.001). Thus, it can be concluded that realization of the course entitled “Diet full of life” specifically relating to young children’s nutrition affected a significant increase in participant knowledge, particularly among the younger age groups and people with higher education. Kindergarten employees need constant supplemental education in the form of training courses, during which they acquire actual knowledge given in an accessible manner for use in practice. The proper assessment of the supplemental education course presented herein, as well as of the course participants, was performed using General Estimating Equations.

Sign in / Sign up

Export Citation Format

Share Document