Semiparametric regression for clustered data

By Xihong Lin, Raymond J. Carroll. 2001

86 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f1f932052be26648830b3b5c2441a1906cc97abf

How researchers see this paper

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

In Artificial Intelligence

1. Lan Wang
2. Shujie Ma
3. Jianhua Z. Huang
4. Jianqing Fan
5. Qiongxia Song
6. Lan Zhou
7. Lian-Shan Zhang
8. Xuming He

In Statistics and Probability

1. Lan Wang
2. Jianqing Fan
3. Xuming He
4. Jianhua Z. Huang
5. Li Wang
6. Shujie Ma
7. Raymond J. Carroll
8. Zhongyi Zhu

Citations in the corpus

• Semiparametric Regression for Clustered Data Using Generalized Estimating Equations (2001, result): “If Tij is a cluster-level covariate (i.e., Tij D Ti), then, in contrast to the results reported in this paper, Lin and Carroll (2001) showed that the pro lekernel method works as usual and yields a p n consistent and semiparametric ef cient estimate of ‚ if the true covariance is assumed and…”
• Simultaneous variable selection and estimation in semiparametric modeling of longitudinal/clustered data (2013, methodology): “When mis and Σis are the same for all individuals, Carroll et al. [3] considered the efficient estimation of β in model (1) using local linear smooth backfitting.”
• 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…”
• 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.”
• 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.”
• General design Bayesian generalized linear mixed models (2006, methodology): “Analyses of these data have appeared previously in the literature (e.g., Diggle, Liang and Zeger, 1994; Lin and Carroll, 2001), so our description of them will be brief.”
• Efficient Semiparametric Marginal Estimation for Longitudinal/Clustered Data (2005, methodology): “Lin and Carroll (2001a) allowed different working covariances in their estimating equations of θ(t) and β .”
• Nonparametric inference with generalized likelihood ratio tests (2007, methodology): “19), and Murphy (1993) revealed a similar type of result for Cox’s proportional hazards model using a simple sieve method (piecewise constant approximation to a smooth function).”