Improving generalised estimating equations using quadratic inference functions

By Annie Qu, Bruce G. Lindsay, Bing Li. 2000

449 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/68a69b1153b69b52cee6b0bd0c8432e3d023f73b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. Annie Qu
2. Lan Xue
3. Chenlei Leng
4. Cheng Yong Tang
5. Tze Leung Lai
6. Dylan S. Small
7. Lan Wang
8. Jianhui Zhou

In Statistics and Probability

1. Annie Qu
2. Chenlei Leng
3. Dylan S. Small
4. Tze Leung Lai
5. Cheng Yong Tang
6. Lan Wang
7. Jianhui Zhou
8. Lan Xue

Citations in the corpus

• Longitudinal Data Analysis. Handbooks of Modern Statistical Methods (2009, background): “Furthermore, similar Gaussian or “quadratic” estimating equations have been proposed by Crowder (1995) and Qu, Lindsay, and Li (2000).”
• Consistent Model Selection for Marginal Generalized Additive Model for Correlated Data (2010, methodology): “In addition, within-cluster correlation is incorporated to obtain efficient parameter estimation via the quadratic inference function (Qu et al., 2000).”
• Marginal regression analysis of longitudinal data with time‐dependent covariates: a generalized method‐of‐moments approach (2007, methodology): “…are combining the same estimating equations, the GMM has the same asymptotic efficiency as GEEs if the working correlation structure in the GEE method is correctly specified but the GMM is more asymptotically efficient when the working correlation in the GEEs is misspecified (Qu et al., 2000).”
• Joint composite estimating functions in spatiotemporal models (2012, methodology): “This approach is similar to the generalized method of moments (GMM) (Hansen, 1982) and the quadratic inference function (Qu et al., 2000).”
• Penalized empirical likelihood and growing dimensional general estimating equations (2012, methodology): “These agree with the general conclusion in Qu et al. (2000).”
• Consistent model selection and data‐driven smooth tests for longitudinal data in the estimating equations approach (2009, methodology): “The approach that is developed in this paper is based on the quadratic inference function (QIF) that was proposed by Qu et al. (2000), which generalizes the generalized method of Address for correspondence: Lan Wang, School of Statistics, 385 Ford Hall, University of Minnesota, 224 Church Street…”
• Building adaptive estimating equations when inverse of covariance estimation is difficult (2003, methodology): “…the particular m1 and m2 given above by any set of estimating functions where dim.m1/ = p:We shall show how to choose an optimal linear combination of the elements ofm2 to add tom1: Thus we could apply this dimension reduction technique to the working correlation scores of Qu et al. (2000) as well.”
• Generalized Estimating Equations in Longitudinal Data Analysis: A Review and Recent Developments (2014, unknown): “i with r i = Y i −μ i is an estimator of the variancecovariance matrix of Y i [6, 23].”
• Estimation and model selection in generalized additive partial linear models for correlated data with diverging number of covariates (2014, methodology): “The quadratic inference function (QIF) [23] utilizes within-cluster correlation into account without specifying the likelihood function, and is less sensitive to the misspecification of working correlation matrices compared to the generalized estimating equation (GEE) method [19], in general.”