Semiparametric and Nonparametric Regression Analysis of Longitudinal Data

By Danyu Lin, Zhiliang Ying. 2001

308 citations recorded by the source corpus.

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Citations in the corpus

Nonparametric inference with generalized likelihood ratio tests (2007, methodology): “The model has been studied by Zhang, Lee and Song (2002) and Li et al. (2002) in regression setting and by Lin and Carroll (2001a,b), Lin and Ying (2001), and Fan and Li (2004) for the analysis of longitudinal data.”
Semiparametric Time‐Varying Coefficients Regression Model for Longitudinal Data (2005, methodology): “…K0ðuÞÞ þ Opðn1=2b2Þ þ opð1Þ; ð33Þ where the last equality is obtained by the condition that k0(t) and b(t) are continuous and have bounded variations, that n1=2ðbK0ðtÞ K0ðtÞÞ converges weakly to a zero mean process with continuous sample paths and by applying lemma A.1 of Lin & Ying (2001).”
Asymptotic optimality and efficient computation of the leave-subject-out cross-validation (2012, background): “Various semiparametric [e.g., Zeger and Diggle (1994), Zhang et al. (1998), Lin and Ying (2001), Wang, Carroll and Lin (2005)] and nonparametric [e.g., Fan and Zhang (2000), Lin and Carroll (2000), Rice and Silverman (1991), Wang (1998, 2003), Welsh, Lin and Carroll (2002), Zhu, Fung and He (2008)]…”
Co-Benefits and Additionality of the Clean Development Mechanism: An Empirical Analysis (2011, unknown): “With this model (12) can be converted to a nonparametric fixed effect regressionˆy it = m(w it ) + u i + ε it , (15) Multiple methods are available to estimate this model including the series method and the profile-kernel method (Lin and Ying, 2001; Su and Ullah, 2006).”
Statistical Methods with Varying Coefficient Models. (2008, methodology): “Lin and Ying (2001) employed a counting process approach which is ameliorated by Fan and Li (2004) .”
Functional Adaptive Model Estimation (2005, methodology): “For example, standard linear or logistic regression with functional predictors, as well as far more complicated models, can easily be applied using this approach.”
Simultaneous inference of linear models with time varying coefficients (2010, methodology): “The model has been studied in Hoover et al. (1998), Wu et al. (1998), Fan and Zhang (2000), Lin and Ying (2001), Huang et al. (2004) and Ramsay and Silverman (2005) among others.”
Model averaging based on leave-subject-out cross-validation (2016, background): “, 2008) and semiparametric longitudinal data models (Zeger and Diggle, 1994; Zhang et al., 1998; Lin and Ying, 2001) have also been considered.”
Conditional growth charts (2006, result): “ar X iv :m at h/ 07 02 63 4v 1 [ m at h. ST ] 2 2 Fe b 20 07 The Annals of Statistics 2006, Vol. 34, No. 5, 2069–2097 DOI: 10.1214/009053606000000623 c© Institute of Mathematical Statistics, 2006 DISCUSSION PAPER CONDITIONAL GROWTH CHARTS1 By Ying Wei and Xuming He”
Generalized linear models with functional predictors (2002, methodology): “Hoover et al. (1998), Wu et al. (1998) and Lin and Ying (2001) used the varying-coefficient models proposed in Hastie and Tibshirani (1993) to extend equation (2) by allowing the regression coefficients to vary over time.”
Regression Analysis of Longitudinal Data in the Presence of Informative Observation and Censoring Times (2007, methodology): “In this case, using the approach of Lin and Ying (2001), who studied models (1) and (2) without the latent variable V , we obtained β̂1 = −.1946 and β̂2 = .0492 with estimated standard errors of .0456 and .0131.”
Inference of time-varying regression models (2012, background): “Varying coefficient models have been extensively studied in the literature, and they are useful for characterizing nonconstancy relationship between predictors and responses in regression models; see, for example, [19, 20, 28, 29, 33, 44, 52, 55].”
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…”
Handling drop‐out in longitudinal studies (2004, background): “The vector Yi also can be viewed as the realization of a stochastic process {Yi(t)} at "xed time points t1; : : : ; tT , and is sometimes modelled this way [30, 31].”
Estimation in Partially Linear Models With Missing Covariates (2004, methodology): “Finally, we show that our methods can be extended to longitudinal and clustered data when working independence is used as the method of estimation, thus extending the work on nonparametric regression for correlated data using working independence (Zeger and Diggle 1994; Hoover, Rice, Wu, and Yang 19”
Joint Analysis of Longitudinal Data With Informative Observation Times and a Dependent Terminal Event (2012, methodology): “For comparison, we also considered the original method of Lin and Ying (2001) (denoted by LY), the method of Sun, Sun, and Liu (2007) (denoted by SSL), and the method of Liu, Huang, and O’Quigley (2008) (denoted by LHO), using the same setup as in Table 1 but with n = 200.”