Estimation in a semiparametric model for longitudinal data with unspecified dependence structure

By Xuming He, Zhongyi Zhu, Wing Kam Fung. 2002

266 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/31ae9ab0fe4098e275b4a96514b23ed9713b9ae6

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In Statistics and Probability

Citations in the corpus

Robust Estimation in Generalized Partial Linear Models for Clustered Data (2005, methodology): “Following theorem 2 of He et al. (2002), K and S can be consistently estimated by n−1 times K̂n = m∑ i=1 X∗Ti iX∗i (12) and Ŝn = m∑ i=1 X∗Ti Ti V−1i hi(µi)h T i (µi)V −1 i iX ∗ i , (13) where all of the quantities involved are evaluated at θ̂ .”
Robust estimates in generalized partially linear models (2006, methodology): “The basic ideas from robust smoothing and robust regression estimation have been adapted to partially linear regression models where H(t) = t; we refer to [3, 13, 15].”
Efficient Estimation in Marginal Partially Linear Models for Longitudinal/Clustered Data Using Splines (2007, methodology): “Previous application of spline approximations in longitudinal data modelling includes He et al. (2002) and Huang et al. (2002, 2004).”
Conditional growth charts (2006, methodology): “Similar partitions and standardizations were used in [21].”
Semiparametric Mean–Covariance Regression Analysis for Longitudinal Data (2009, background): “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).”
GEE analysis of clustered binary data with diverging number of covariates (2011, methodology): “…“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 (2006) and…”
Practical Confidence Intervals for Regression Quantiles (2005, unknown): “He, Zhu, and Fung (2002) gave another alternative by replacing fi(0) by (ψτ (ri + dn) − ψτ (ri − dn))/2dn for some sequence dn tending to 0 as n→∞, where ψτ (r) = τI(r > 0) + (τ − 1)I(r 0) is the derivative of ρτ , but this option is not yet available in any package.”
An Approach to Multivariate Covariate-Dependent Quantile Contours With Application to Bivariate Conditional Growth Charts (2008, methodology): “If we approximate all of the coefficient functions by splines, then models (6) and (7) are known as varying-coefficient quantile regression models; these have been studied by Kim (2007) and Cai and Xu (2007).”
Robust estimation in generalized semiparametric mixed models for longitudinal data (2007, methodology): “The inference in GPLM or GLMM can be made by maximum likelihood estimation (MLE) and generalized estimating equations (GEE) estimation, but the classical methods originated from MLE and GEE can be sensitive to outliers or departure from underlying distributions.”
Quantile regression in partially linear varying coefficient models (2009, methodology): “The proof of Theorem 2 follows the similar arguments to those for Theorem 2 of [10], and thus is omitted.”