1995
DOI: 10.1080/01621459.1995.10476542
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Nonparametric Empirical Bayes Growth Curve Analysis

Abstract: Nonparametric and semiparametric regression have been suggested as alternatives to parametric models for growth curve analysis (Gasser, Muller, Kohler, Molinari, and Prader, 1984;Hart and Wehrly, 1986;Ramsay and Dalzell, 1991; Stii.tzle et al, 1980). In this paper we demonstrate that Empirical Bayes estimation can be used to improve linear smoothing estimates when multiple curves are available.

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Cited by 18 publications
(4 citation statements)
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“…Nonparametric methods using kernel estimators have been considered for smoothing longitudinal or repeatedmeasures data (e.g., Altman 1990;Altman and Casella 1995;Fraiman and Meloche 1994;Hart and Wehrly 1986;and Muller 1988). All of these nonparametric approaches have the common feature that the unknown mean response curve over time is estimated by smoothing the raw data and time is the only explanatory variable.…”
Section: Introductionmentioning
confidence: 99%
“…Nonparametric methods using kernel estimators have been considered for smoothing longitudinal or repeatedmeasures data (e.g., Altman 1990;Altman and Casella 1995;Fraiman and Meloche 1994;Hart and Wehrly 1986;and Muller 1988). All of these nonparametric approaches have the common feature that the unknown mean response curve over time is estimated by smoothing the raw data and time is the only explanatory variable.…”
Section: Introductionmentioning
confidence: 99%
“…The disadvantage of this approach is however, that the characteristic parameter values only display a certain effect on the growth curve and can not in general capture the treatment effect on the growth curve as a whole. We intend to improve grofit to compute the influence of covariates in a more general way, see e.g., Altmann and Casella (1995).…”
Section: Discussionmentioning
confidence: 99%
“…To investigate the dynamic relationship between repeated measurements in an outcome process and covariates, various inference procedures have been developed in the form of regression analysis over the last several decades. This includes parametric methods for linear and nonlinear regressions (Laird, Donnelly, and Ware 1992; Diggle, Liang, and Zeger 1994; Davidian and Giltinan 1995), nonparametric regressions with kernel, spline and other smoothing techniques (Rice and Silverman 1991; Altman and Casella 1995; Silverman 1996; Lin and Carroll 2000, 2001), smoothing methods for non-parametric regressions with smooth time-varying coefficients (Hoover, Rice, Wu, and Yang 1998; Wu, Chiang, and Hoover 1998; Huang, Wu and Zhou 2002, 2004; Xue and Zhu 2007), functional linear models (Fan and Zhang 2000; James, Wang and Zhu 2009), and inference methods on semiparametric regressions (Moyeed and Diggle 1994; Zeger and Diggle 1994; Lin and Ying 2001; Lin and Carroll 2006; Li 2011). In particular, semiparametric regression models follow the idea of the Cox (1972) proportional hazards model and keep the regression form of covariate effects while leave the baseline (or intercept) unspecified, hence remove limitations of parametric and nonparametric regressions in practical analysis of longitudinal observations (Wu, Chiang and Hoover 1998).…”
Section: Introductionmentioning
confidence: 99%