Nonparametric Regression Analysis of Longitudinal Data

By Jane-ling Wang. 2005

247 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e78086c04f37c3cc65e8e75d1a3c9e25fe317885

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Citations in the corpus

Backfitting Random Varying‐Coefficient Models with Time‐Dependent Smoothing Covariates (2004, background): “…for more flexible functional dependence of the response variable on the covariates, see, for example, Zeger & Diggle (1994), Shi et al. (1996), Staniswalis & Lee (1998), Hoover et al. (1998), Wu et al. (1998), Zhang et al. (1998), Lin & Carroll (2000), Fan & Zhang (2000), Rice & Wu (2001), Wu…”
Fast bivariate P‐splines: the sandwich smoother (2010, methodology): “, 2005 or Yao and Lee, 2006) while other smoothing methods such as kernel (Staniswalis and Lee, 1998) and penalized splines (Di et al.”
Methodology and convergence rates for functional linear regression (2007, background): “There is a small, but increasing, literature on applications of functional regression to longitudinal data analysis; see, for example, [6, 27].”
Modelling sparse generalized longitudinal observations with latent Gaussian processes (2008, background): “Various aspects of the relationship between functional and longitudinal data are discussed in Staniswalis and Lee (1998), Rice (2004) and Zhao and Marron (2004); an early study of modeling longitudinal trajectories in biological applications with functional principal components is Kirkpatrick and…”
Bernstein polynomial probability density estimation (2004, unknown): “See, for example, Gasser and Müller (1979), Gasser et al. (1985), and Müller (1988, 1991).”
Functional Modelling and Classification of Longitudinal Data * (2005, methodology): “Non-parametric implementations with kernel-type smoothing methods were developed in Capra & Müller (1997) and Staniswalis & Lee (1998).”
Functional quasi‐likelihood regression models with smooth random effects (2003, methodology): “E-mail: mueller@wald.ucdavis.edu models include the development of functional analysis-of-variance approaches (Brumback and Rice, 1998; Staniswalis and Lee, 1998) and adaptations of varying-coefficient regressionmodels (Hoover et al., 1998).”
Varying-coefficient functional linear regression (2010, background): “This phenomenon was observed in Yao et al. (2005b) and is due to the fact that, according to (2.”
Polynomial Spline Estimation and Inference for Varying Coefficient Models with Longitudinal Data (2003, background): “…allow for nonparametric covariate effects; see, for example, Hart and Wehrly (1986), Rice and Silverman (1991), Zeger and Diggle (1994), Moyeed and Diggle (1994), Besse, Cardot and Ferraty (1997), Brumback and Rice (1998), Staniswalis and Lee (1998), Cheng and Wei (2000) and Lin and Carroll (2000).”
Functional linear regression analysis for longitudinal data (2005, methodology): “We model the actually observed data which consist of sparse and irregular repeated measurements of the predictor and response trajectories Xi and Yi, contaminated with additional measurement errors (see [28, 30]).”
Adaptive bandwidth choice (2003, background): “…to get the Bias( f̂ (x)) to be O(h p) dates back to Parzen (1962) and Bartlett (1963); some more recent references on ‘higher-order’ kernels include the following: Devroye (1987; 1992), Gasser et al. (1985), Granovsky and Müller (1991), Jones (1995), Marron (1994), Müller (1988) and Scott (1992).”
Asymptotic distributions of nonparametric regression estimators for longitudinal or functional data (2007, methodology): “This problem was also discussed by Staniswalis and Lee [12] and Lin and Carroll [9], where they used the heuristic arguments of the local property of local polynomial estimation and intuitively ignored the within-subject correlation while deriving the asymptotic variances.”
Assessing the finite dimensionality of functional data (2006, background): “…field is very extensive; it extends well into related areas, such as longitudinal data analysis, and is discussed in these books and in papers by Capra and Müller (1997), Brumback and Rice (1998), Staniswalis and Lee (1998), Cardot et al. (1999, 2003) and He et al. (2003), to list only a very few.”
Functional Data Analysis (2016, methodology): “For the much more diﬃcult but commonly encountered situation of sparse functional data, the FPCA approach was investigated in Shi, Weiss & Taylor (1996); Staniswalis & Lee (1998); James, Hastie & Sugar (2000); Rice & Wu (2001); Yao, M¨uller & Wang (2005a); Yao & Lee (2006); and Paul & Peng (2009).”
Efficient Estimation in Marginal Partially Linear Models for Longitudinal/Clustered Data Using Splines (2007, background): “An incomplete list of publications include 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…”
A general framework for functional regression modelling (2017, background): “assumption along t and then smoothing the empirical covariance of the centred process leaving out the diagonal, which is contaminated by the variance of εit (Staniswalis and Lee, 1998; Yao et al., 2005a).”
MULTILEVEL FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS (2009, methodology): “nd G B(t,s), denoted by Gˆ T (t,s) and Gˆ B(t,s), respectively. The third step is to estimate Kˆ T(t,s) by smoothing Gˆ T(t,s) for t 6= s. The idea of dropping diagonal elements in smoothing was from Staniswalis and Lee (1998) and Yao, Mu¨ller and Wang (2005). An inspection of equation (2.5) reveals”