scholarly journals Quadratic inference functions in marginal models for longitudinal data

2009 ◽  
Vol 28 (29) ◽  
pp. 3683-3696 ◽  
Author(s):  
Peter X.-K. Song ◽  
Zhichang Jiang ◽  
Eunjoo Park ◽  
Annie Qu
Keyword(s):  
Longitudinal Data ◽  
Marginal Models ◽  
Quadratic Inference Functions

scholarly journals Reviewing of Different Methods for Handling Longitudinal Count data

2021 ◽  
Vol 23 (08) ◽  
pp. 195-206
Author(s):  
Amany. M ◽  
◽  
Mousa ◽  
Ahmed. A ◽  
El sheikh ◽  
...  
Keyword(s):  
Method Of Moments ◽  
Count Data ◽  
Generalized Estimating Equations ◽  
Generalized Method Of Moments ◽  
Marginal Models ◽  
Advantages And Disadvantages ◽  
Population Average ◽  
Longitudinal Count Data ◽  
Quadratic Inference Functions ◽  
Primary Focus

In this paper, we will review the methods that used to handle longitudinal data in the case of marginal models when inferences about the population average are the primary focus [1] or when future applications of the results require the expectation of the response as a function of the current covariates [7]. We will review the generalized estimating equations method (GEE), quadratic inference functions (QIF), generalized quasi likelihood (GQL) and the generalized method of moments (GMM). These methods will be reviewed by discussing its advantages and disadvantages in more details.

scholarly journals Penalized Quadratic Inference Function-Based Variable Selection for Generalized Partially Linear Varying Coefficient Models with Longitudinal Data

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jinghua Zhang ◽  
Liugen Xue
Keyword(s):  
Variable Selection ◽  
Longitudinal Data ◽  
Linear Models ◽  
Selection Procedure ◽  
Real Data ◽  
Finite Sample ◽  
Varying Coefficient ◽  
Partially Linear ◽  
Quadratic Inference Function ◽  
Quadratic Inference Functions

Semiparametric generalized varying coefficient partially linear models with longitudinal data arise in contemporary biology, medicine, and life science. In this paper, we consider a variable selection procedure based on the combination of the basis function approximations and quadratic inference functions with SCAD penalty. The proposed procedure simultaneously selects significant variables in the parametric components and the nonparametric components. With appropriate selection of the tuning parameters, we establish the consistency, sparsity, and asymptotic normality of the resulting estimators. The finite sample performance of the proposed methods is evaluated through extensive simulation studies and a real data analysis.

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