KERNEL SMOOTHING ON VARYING COEFFICIENT MODELS WITH LONGITUDINAL DEPENDENT VARIABLE

By Colin O. Wu, Chin‐Tsang Chiang. 2000

155 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5d152733314e2c906b920502d91dca2af191fe62

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Citations in the corpus

Semiparametric and Nonparametric Regression Analysis of Longitudinal Data (2001, background): “Wu and Chiang (2000) improved the asymptotic properties of a class of kernel estimators by weighting each person’s measure inversely to Ni .”
Covariate Adjusted Correlation Analysis via Varying Coefficient Models (2005, methodology): “…have proposed smoothing methods based on local least squares and smoothing splines, and recent approaches include a componentwise kernel method (Wu & Chiang, 2000), a componentwise spline method (Chiang et al., 2001) and a method based on local maximum likelihood estimates (Cai et al., 2000).”
Functional Regression (2014, unknown): “Wu & Chiang (2000) fit a general linear functional response regression model, assuming iid errors, no random effect functions, and using kernel smoothing to regularize the functional coefficients, and Chiang et al. (2001) proposed smoothing spline regularization for the same model.”
A UNIFIED VARIABLE SELECTION APPROACH FOR VARYING COEFFICIENT MODELS (2012, methodology): “In the last decade, many estimation and hypothesis testing methods have been developed for VC models; see for example Wu and Chiang (2000), Huang, Wu, and Zhou (2002) and Kim (2007).”
Empirical Likelihood for a Varying Coefficient Model With Longitudinal Data (2007, methodology): “We chose RAEL for comparison with NA because Wu et al. (1998) already used the normal approximation to construct the confidence bands.”
Inference for covariate adjusted regression via varying coefficient models (2006, methodology): “For varying coefficient models, Hoover, Rice, Wu and Yang [6] have proposed smoothing methods based on local least squares and smoothing splines, and recent approaches include a componentwise kernel method [13], a componentwise spline method [2] and a method based on local maximum likelihood estimat”
Generalized varying coefficient models for longitudinal data (2008, background): “Estimation of the time-varying coefficient functions involves not more than one-dimensional smoothing (Hoover, Rice, Wu & Yang, 1998; Fan & Zhang, 2000; Wu, Yu & Chiang, 2000; Wu & Chiang, 2000; Chiang, Rice & Wu, 2001; and Huang, Wu & Zhou, 2004).”
Functional Data Analysis for Sparse Longitudinal Data (2005, background): “Fan and Zhang (2000) and Wu and Chiang (2000) analyzed these data with varying coef ficient models adapted to longitudinal data, and Diggle, Liang, and Zeger (1994) discussed classical longitudinal approaches for these data.”
Statistical Methods with Varying Coefficient Models. (2008, methodology): “The approaches for constructing confidence regions based on the kernel method can be found in Wu and Chiang (2000) and Chiang et al. (2001).”
Varying Coefficient Regression Models: A Review and New Developments (2015, methodology): “Wu & Chiang (2000) suggested a componentwise kernel smoothing method in case the covariate X is time-invariant.”
Functional single index models for longitudinal data (2011, unknown): “In their analysis, only the effect of pre-infection CD4 percentage was found to be significant and positive, but none of the covariate effects seem time-dependent [see Figures 1 and 2 in Wu and Chiang (2000)].”
A Geometric Approach to Maximum Likelihood Estimation of the Functional Principal Components From Sparse Longitudinal Data (2007, methodology): “This dataset has been analyzed using various approaches, including varying coefficient models (Fan and Zhang 2000; Wu and Chiang 2000), functional principal component approach (Yao, Müller, and Wang 2005), and parametric random effects models (Diggle et al.”
Functional Varying Coefficient Models for Longitudinal Data (2010, methodology): “These methods include polynomial spline (Huang, Wu and Zhou 2004) and smoothing spline (Chiang, Rice and Wu 2001) approaches, as well as local polynomial smoothing (Fan and Zhang 2000; Wu and Chiang 2000).”
Functional Data Analysis (2016, methodology): “In addition to such a two-step procedure, one-step smoothing methods have been also studied (Hoover et al. 1998; Wu & Chiang 2000; Eggermont, Eubank & LaRiccia 2010; Huang, Wu & Zhou 2002), as well as hypothesis testing and conﬁdence bands (Wu, Chiang & Hoover 1998; Huang, Wu & Zhou 2004), with…”