Semiparametric Regression for Clustered Data Using Generalized Estimating Equations

By Xihong Lin, Raymond J. Carroll. 2001

303 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4cb48aa2f7f35be22fa9d5b11b37fe9ddd7606a8

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Citations in the corpus

Nonparametric inference with generalized likelihood ratio tests (2007, methodology): “The model has been studied by Zhang et al. (2002) and Li et al. (2002) in regression setting and by Lin and Carroll (2001a, 2001b) , Lin and Ying (2001), and Fan and Li (2004 )f or the analysis of longitudinal data.”
Semiparametric estimation in general repeated measures problems (2006, methodology): “ Lin and Carroll (2001) and Wang et al. (2005) proposed estimating-equation-based profile kernel methods for a marginal generalized semiparametric model that was similar to the normal model (2) for clustered data.”
Efficient Semiparametric Marginal Estimation for Longitudinal/Clustered Data (2005, methodology): “Following Lin and Carroll (2001b), it can be shown that once such an estimator of τ converges in probability to some τ∗ at a √ n rate, then there is no asymptotic effect on our estimators of β and θ(·) due to estimation of τ ; that is, Proposition 2 still holds. In addition, with τ being of finite d”
Conditional growth charts (2006, result): “If the error terms are independent of τ and of the covariates in the model, then models of this form have been well studied in the longitudinal data analysis by many other authors, including Chiang, Rice and Wu [4], Lin and Carroll [24], Lin and Ying [23] and Fan and Li [11], to mention just a few.”
Estimation in Partially Linear Models With Missing Covariates (2004, background): “There is, however, a pro le quasi-likelihood estimating equation de ned in an iterative fashion (Severini and Staniswalis 1994; Lin and Carroll 2001).”
Focused information criterion and model averaging for generalized additive partial linear models (2011, methodology): “1 GAPLM with a single nonparametric component, the generalized partial linear model (GPLM), has been well studied in the literature; see, for example, Severini and Staniswalis (1994), Lin and Carroll (2001), Hunsberger (1994), Hunsberger et al. (2002) and Liang (2008).”
Profile likelihood estimation of partially linear panel data models with fixed effects (2006, background): “A2 is assumed to simplify the proof and it is also assumed in Lin and Carroll (2001) and Hu et al. (2004).”
Nonparametric Regression Analysis of Longitudinal Data (2005, background): “Additional results on semiparametric marginal models are also available in these two papers and [15] and [18].”
Functional Varying Coefficient Models for Longitudinal Data (2010, background): “While there are well established results for incorporating within subject correlation to increase efficiency for parametric models, much less work exists for nonparametric approaches (Lin and Carroll 2001; Qu and Li 2006; Fan, Huang and Li 2007).”
GEE analysis of clustered binary data with diverging number of covariates (2011, methodology): “In the classical “ﬁxed p ” setting, GEE with partially linear model speciﬁcation has been investigated by Lin and Carroll (2001a, 2001b), Lin and Ying (2001), He, Zhu and Fung (2002), Fan and Li (2004), Chiou and M¨uller (2005), Wang, Carroll and Lin (2005), Chen and Jin (2006), He, Fung and Zhu…”
Efficient Estimation in Marginal Partially Linear Models for Longitudinal/Clustered Data Using Splines (2007, methodology): “(16) The efficient score and information bound here are derived without the normal distribution assumption, but they coincide with those obtained by Lin & Carroll (2001a) under the multivariate normality assumption.”
Robust Estimation in Generalized Partial Linear Models for Clustered Data (2005, methodology): “Lin and Carroll (2001a) considered the GPLM for clustered data when tij is invariant in j so that it is a subject-level covariate.”