New Estimation and Model Selection Procedures for Semiparametric Modeling in Longitudinal Data Analysis

By Jianqing Fan, Runze Li. 2004

469 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c3a854d4178a29906cc9f9ba2311f7a48a72f1cd

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In Statistics and Probability

Citations in the corpus

NONCONCAVE PENALIZED M-ESTIMATION WITH A DIVERGING NUMBER OF PARAMETERS (2011, methodology): “The nonconcave likelihood approach has been further extended to Cox’s model for survival data (Fan and Li (2002)), partially linear models for longitudinal data (Fan and Li (2004)) and varying coefficient partially linear models (Li and Liang (2008)).”
Journal of the American Statistical Association Linear or Nonlinear? Automatic Structure Discovery for Partially Linear Models Linear or Nonlinear? Automatic Structure Discovery for Partially Linear Models (unknown): “…Hong 1991; Green and Silverman 1994; Liang, Hardle, and Carroll 1999), penalized regression splines (Ruppert, Wand, and Carroll 2003; Liang 2006; Wang, Li, and Huang 2008), kernel smoothing (Speckman 1988), and local polynomial regression (Fan and Gijbels 1996; Fan and Li 2004; Li and Liang 2008).”
An asymptotic theory for model selection inference in general semiparametric problems (2007, unknown): “Fan and Li (2004) use local polynomial estimators in a longitudinal data setting and select”
Statistical Estimation in Generalized Multiparameter Likelihood Models (2009, background): “…when both are asymptotically normal (Severini and Wong 1992), but on the other hand, there are situations for which Theorem 1 holds but the profile likelihood estimator does not work because it requires existence of the least favorable curves (see the example discussed in Fan and Wong 2000).”
Semiparametric estimation in general repeated measures problems (2006, methodology): “Lin and Carroll (2000, 2001) and Fan and Li (2004) made an effort to incorporate the correlation structure in the estimation procedure within the traditional kernel framework.”
Efficient Estimation in Marginal Partially Linear Models for Longitudinal/Clustered Data Using Splines (2007, unknown): “…Brumback & Rice (1998), Staniswalis & Lee (1998), Hoover et al. (1998), Wu et al. (1998), Fan & Zhang (2000), Lin & Carroll (2000), Chiang et al. (2001), Lin & Ying (2001), Martinussen & Scheike (2001), Wu & Zhang (2002), Wu & Liang (2004), Fan & Li (2004), Sun & Wu (2005), among many others.”
Partially linear hazard regression with varying coefficients for multivariate survival data (2008, background): “Variable selections based on the non-concave penalty likelihood can also be developed for the partly linear hazard model along the framework of Fan and Li (2004). An ongoing research will focus on testing whether the coefficient functions are constants or of certain parametric forms.”
Shrinkage Estimation of the Varying Coefficient Model (2009, methodology): “In our current work, we follow the method of Fan and Li (2004) and select those two parameters separately.”
Joint Analysis of Longitudinal Data With Informative Observation Times and a Dependent Terminal Event (2012, methodology): “Various methods for analyzing these data have been proposed (Welsh, Lin, and Carroll 2002; Davis 2002; Fitzmaurice, Laird, and Ware 2004; Fan and Li 2004).”
GEE analysis of clustered binary data with diverging number of covariates (2011, unknown): “…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 Huang, Zhang and Zhou…”
Semiparametric Time‐Varying Coefficients Regression Model for Longitudinal Data (2005, background): “In their paper, Fan & Li (2004) proposed a goodness-of-fit method for the intercept when other covariate effects are constant.”
Conditional growth charts (2006, result): “If the error terms are independent of � and of the covariates in the model, then models of this form have been well studied in the longitudinal data analysis by many other authors, including Chiang, Rice and Wu [4], Lin and Carroll [24], Lin and Ying [23] and Fan and Li [ 11 ], to mention just a few”
Nonparametric inference with generalized likelihood ratio tests (2007, methodology): “…have also been widely used in 20 the analysis of longitudinal and functional data (Brumback and Rice, 1998; Carrol, Ruppert and Welsh, 1998; Hoover et al., 1998; Huang, Wu and Zhou, 2002; Fan and Li, 2004) and financial modeling (Härdle, Herwartz and Spokoiny, 2003; Mercurio and Spokoiny, 2004).”