Efficient Semiparametric Marginal Estimation for Longitudinal/Clustered Data

By Naisyin Wang, Raymond J. Carroll, Xihong Lin. 2005

189 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c87cdcee33d5c57042bf33d0365fa2b54b92114e

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. Jianqing Fan
2. Shujie Ma
3. Lan Wang
4. Fang Yao
5. Jane-ling Wang
6. Heng Peng
7. Qiongxia Song
8. Li Wang

In Statistics and Probability

1. Jianqing Fan
2. Heng Peng
3. Shujie Ma
4. Lan Wang
5. Li Wang
6. H. Muller
7. Cun-Hui Zhang
8. Ming-Yen Cheng

Citations in the corpus

• GEE analysis of clustered binary data with diverging number of covariates (2011, background): “…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 (2006) and Huang, Zhang and Zhou (2007), among others.”
• New Estimation and Model Selection Procedures for Semiparametric Modeling in Longitudinal Data Analysis (2004, methodology): “The local linear estimate is simply ˆ α(t0; β) =ˆ a0. To improve the efficiency, Wang, Carroll, and Lin (2004) recently proposed an innovative iterated profile likelihood method for estimating β when the true covariance function is given.”
• Nonparametric independence screening and structure identification for ultra-high dimensional longitudinal data (2013, background): “It is well known that the working independence estimator β̃PLS 1 is not semiparametric efficient when the error process is dependent [32].”
• Penalized spline models for functional principal component analysis (2006, background): “…best of our knowledge, no asymptotic consistency results of penalized spline models for functional data are available so far, whereas kernel and smoothing spline approaches for clustered data have been investigated by Lin and Carroll (2000), Wang (2003), Lin et al. (2004) and Wang et al. (2005).”
• A Confidence Corridor for Sparse Longitudinal Data Curves (2011, unknown): “This was also observed by Ma et al. (2010), Wang et al. (2005).”
• Nonparametric Regression Analysis of Longitudinal Data (2005, methodology): “Another framework mimicking the marginal approach of GEE (Generalized Estimating Equations) can be found in [14], [26], [27] and”
• Efficient Estimation in Marginal Partially Linear Models for Longitudinal/Clustered Data Using Splines (2007, methodology): “In the second example, we use exactly the same setup as used in Wang et al. (2005).”
• Simultaneous variable selection and estimation in semiparametric modeling of longitudinal/clustered data (2013, methodology): “Both Wang, Carroll and Lin [35] and Huang, Zhang and Zhou [14] analyzed the same dataset using a PLM.”
• Semiparametric Mean–Covariance Regression Analysis for Longitudinal Data (2009, unknown): “See, for example, the kernel and spline approaches in Lin and Carroll (2001), Welch, Lin, and Carroll (2002), He, Zhu, and Fung (2002), Wang (2003), Wang, Carroll, and Lin (2005), He, Fung, and Zhu (2005), and Lin and Carroll (2006).”