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55
2005a) Functional data analysis for sparse longitudinal data
 J. Am. Statist. Assoc
"... 54448 and DMS0406430. We are grateful to an Associate Editor and two referees for insightful We propose a nonparametric method to perform functional principal components analysis for the case of sparse longitudinal data. The method aims at irregularly spaced longitudinal data, where the number of r ..."
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Cited by 99 (24 self)
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54448 and DMS0406430. We are grateful to an Associate Editor and two referees for insightful We propose a nonparametric method to perform functional principal components analysis for the case of sparse longitudinal data. The method aims at irregularly spaced longitudinal data, where the number of repeated measurements available per subject is small. In contrast, classical functional data analysis requires a large number of regularly spaced measurements per subject. We assume that the repeated measurements are randomly located with a random number of repetitions for each subject, and are determined by an underlying smooth random (subjectspecific) trajectory plus measurement errors. Basic elements of our approach are the parsimonious estimation of the covariance structure and mean function of the trajectories, and the estimation of the variance of the measurement errors. The eigenfunction basis is estimated from the data, and functional principal component score estimates are obtained by a conditioning step. This conditional estimation method is conceptually simple and straightforward to implement. A key step is the derivation of asymptotic consistency and distribution results under mild conditions, using tools from functional analysis.
Functional linear regression analysis for longitudinal data
 Ann. of Statist
, 2005
"... We propose nonparametric methods for functional linear regression which are designed for sparse longitudinal data, where both the predictor and response are functions of a covariate such as time. Predictor and response processes have smooth random trajectories, and the data consist of a small number ..."
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Cited by 61 (7 self)
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We propose nonparametric methods for functional linear regression which are designed for sparse longitudinal data, where both the predictor and response are functions of a covariate such as time. Predictor and response processes have smooth random trajectories, and the data consist of a small number of noisy repeated measurements made at irregular times for a sample of subjects. In longitudinal studies, the number of repeated measurements per subject is often small and may be modeled as a discrete random number and, accordingly, only a finite and asymptotically nonincreasing number of measurements are available for each subject or experimental unit. We propose a functional regression approach for this situation, using functional principal component analysis, where we estimate the functional principal component scores through conditional expectations. This allows the prediction of an unobserved response trajectory from sparse measurements of a predictor trajectory. The resulting technique is flexible
New estimation and model selection procedures for semiparametric modeling in longitudinal data analysis
 J. Am. Statist. Ass
, 2004
"... Semiparametric regression models are very useful for longitudinal data analysis. The complexity of semiparametric models and the structure of longitudinal data pose new challenges to parametric inferences and model selection that frequently arise from longitudinal data analysis. In this article, two ..."
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Cited by 49 (13 self)
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Semiparametric regression models are very useful for longitudinal data analysis. The complexity of semiparametric models and the structure of longitudinal data pose new challenges to parametric inferences and model selection that frequently arise from longitudinal data analysis. In this article, two new approaches are proposed for estimating the regression coefficients in a semiparametric model. The asymptotic normality of the resulting estimators is established. An innovative class of variable selection procedures is proposed to select significant variables in the semiparametric models. The proposed procedures are distinguished from others in that they simultaneously select significant variables and estimate unknown parameters. Rates of convergence of the resulting estimators are established. With a proper choice of regularization parameters and penalty functions, the proposed variable selection procedures are shown to perform as well as an oracle estimator. A robust standard error formula is derived using a sandwich formula and is empirically tested. Local polynomial regression techniques are used to estimate the baseline function in the semiparametric model.
WaveletBased Nonparametric Modeling of Hierarchical Functions in Colon Carcinogenesis
 JASA
, 2003
"... this article we develop new methods for analyzing the data from an experiment using rodent models to investigate the effect of type of dietary fat on O methylguanineDNAmethyltransferase (MGMT), an important biomarker in early colon carcinogenesis. The data consist of observed pro# les over a ..."
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Cited by 35 (14 self)
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this article we develop new methods for analyzing the data from an experiment using rodent models to investigate the effect of type of dietary fat on O methylguanineDNAmethyltransferase (MGMT), an important biomarker in early colon carcinogenesis. The data consist of observed pro# les over a spatial variable contained within a twostage hierarchy, a structure that we dub hierarchical functional data. We present a new method providing a uni# ed framework for modeling these data, simultaneously yielding estimates and posterior samples for mean, individual, and subsamplelevel pro# les, as well as covariance parameters at the various hierarchical levels. Our method is nonparametric in that it does not require the prespeci# cation of parametric forms for the functions and involves modeling in the wavelet space, which is especially effective for spatially heterogeneous functions as encountered in the MGMT data. Our approach is Bayesian; the only informative hyperparameters in our model are effectively smoothing parameters. Analysis of this dataset yields interesting new insights into how MGMT operates in early colon carcinogenesis, and how this may depend on diet. Our method is general, so it can be applied to other settings where hierarchical functional data are encountered
FUNCTIONAL LINEAR REGRESSION THAT’S INTERPRETABLE 1
"... Regression models to relate a scalar Y to a functional predictor X(t) are becoming increasingly common. Work in this area has concentrated on estimating a coefficient function, β(t), with Y related to X(t) through β(t)X(t)dt. Regions where β(t) ̸ = 0 correspond to places where there is a relationshi ..."
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Cited by 25 (4 self)
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Regression models to relate a scalar Y to a functional predictor X(t) are becoming increasingly common. Work in this area has concentrated on estimating a coefficient function, β(t), with Y related to X(t) through β(t)X(t)dt. Regions where β(t) ̸ = 0 correspond to places where there is a relationship between X(t) and Y. Alternatively, points where β(t) = 0indicate no relationship. Hence, for interpretation purposes, it is desirable for a regression procedure to be capable of producing estimates of β(t) that are exactly zero over regions with no apparent relationship and have simple structures over the remaining regions. Unfortunately, most fitting procedures result in an estimate for β(t) that is rarely exactly zero and has unnatural wiggles making the curve hard to interpret. In this article we introduce a new approach which uses variable selection ideas, applied to various derivatives of β(t), to produce estimates that are both interpretable, flexible and accurate. We call our method “Functional Linear Regression That’s Interpretable” (FLiRTI) and demonstrate it on simulated and realworld data sets. In addition, nonasymptotic theoretical bounds on the estimation error are presented. The bounds provide strong theoretical motivation for our approach.
F: Functional additive models
 J Am Stat Assoc
"... In commonly used functional regression models, the regression of a scalar or functional response on the functional predictor is assumed to be linear. This means the response is a linear function of the functional principal component scores of the predictor process. We relax the linearity assumption ..."
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Cited by 23 (6 self)
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In commonly used functional regression models, the regression of a scalar or functional response on the functional predictor is assumed to be linear. This means the response is a linear function of the functional principal component scores of the predictor process. We relax the linearity assumption and propose to replace it by an additive structure. This leads to a more widely applicable and much more flexible framework for functional regression models. The proposed functional additive regression models are suitable for both scalar and functional responses. The regularization needed for effective estimation of the regression parameter function is implemented through a projection on the eigenbasis of the covariance operator of the functional components in the model. The utilization of functional principal components in an additive rather than linear way leads to substantial broadening of the scope of functional regression models and emerges as a natural approach, as the uncorrelatedness of the functional principal components is shown to lead to a straightforward implementation of the functional additive model, just based on a sequence of onedimensional smoothing steps and without need for backfitting. This facilitates the theoretical analysis, and we establish asymptotic
Analysis of Longitudinal Data With Semiparametric Estimation of Covariance Function
, 2005
"... Improving efficiency for regression coefficients and predicting trajectories of individuals are two important aspects in the analysis of longitudinal data. Both involve estimation of the covariance function. Yet challenges arise in estimating the covariance function of longitudinal data collected at ..."
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Cited by 19 (5 self)
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Improving efficiency for regression coefficients and predicting trajectories of individuals are two important aspects in the analysis of longitudinal data. Both involve estimation of the covariance function. Yet challenges arise in estimating the covariance function of longitudinal data collected at irregular time points. A class of semiparametric models for the covariance function by that imposes a parametric correlation structure while allowing a nonparametric variance function is proposed. A kernel estimator for estimating the nonparametric variance function is developed. Two methods for estimating parameters in the correlation structure—a quasilikelihood approach and a minimum generalized variance method—are proposed. A semiparametric varying coefficient partially linear model for longitudinal data is introduced, and an estimation procedure for model coefficients using a profile weighted least squares approach is proposed. Sampling properties of the proposed estimation procedures are studied, and asymptotic normality of the resulting estimators is established. Finitesample performance of the proposed procedures is assessed by Monte Carlo simulation studies. The proposed methodology is illustrated with an analysis of a real data example. KEY WORDS: Kernel regression; Local linear regression; Profile weighted least squares; Semiparametric varying coefficient model.
Di: Generalized Multilevel Functional Regression 1561
 Journal of the Royal Statistical Society, Ser. B
, 2006
"... We introduce Generalized Multilevel Functional Linear Models (GMFLMs), a novel statistical framework for regression models where exposure has a multilevel functional structure. We show that GMFLMs are, in fact, generalized multilevel mixed models. Thus, GMFLMs can be analyzed using the mixed effects ..."
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Cited by 15 (7 self)
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We introduce Generalized Multilevel Functional Linear Models (GMFLMs), a novel statistical framework for regression models where exposure has a multilevel functional structure. We show that GMFLMs are, in fact, generalized multilevel mixed models. Thus, GMFLMs can be analyzed using the mixed effects inferential machinery and can be generalized within a wellresearched statistical framework. We propose and compare two methods for inference: (1) a twostage frequentist approach; and (2) a joint Bayesian analysis. Our methods are motivated by and applied to the Sleep Heart Health Study, the largest community cohort study of sleep. However, our methods are general and easy to apply to a wide spectrum of emerging biological and medical datasets. Supplemental materials for this article are available online.
Functional coefficient regression models for nonlinear time series: A polynomial spline approach
 Scandinavian Journal of Statistics
, 2004
"... ABSTRACT. We propose a global smoothing method based on polynomial splines for the estimation of functional coefficient regression models for nonlinear time series. Consistency and rate of convergence results are given to support the proposed estimation method. Methods for automatic selection of t ..."
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Cited by 14 (0 self)
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ABSTRACT. We propose a global smoothing method based on polynomial splines for the estimation of functional coefficient regression models for nonlinear time series. Consistency and rate of convergence results are given to support the proposed estimation method. Methods for automatic selection of the threshold variable and significant variables (or lags) are discussed. The estimated model is used to produce multistepahead forecasts, including interval forecasts and density forecasts. The methodology is illustrated by simulations and two real data examples. Key words: forecasting, functional autoregressive model, nonparametric regression, threshold autoregressive model, varying coefficient model
Estimation in partially linear models with missing covariates
 Journal of the American Statistical Association
, 2004
"... The partially linear model Y DXT¯C º.Z/C has been studied extensively when data are completely observed. In this article, we consider the case where the covariate X is sometimes missing, with missingness probability depending on.Y;Z/. New methods are developed for estimating ¯ and º.¢/. Our method ..."
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Cited by 12 (2 self)
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The partially linear model Y DXT¯C º.Z/C has been studied extensively when data are completely observed. In this article, we consider the case where the covariate X is sometimes missing, with missingness probability depending on.Y;Z/. New methods are developed for estimating ¯ and º.¢/. Our methods are shown to outperform asymptotically methods based only on the complete data. Asymptotic ef ciency is discussed, and the semiparametric ef cient score function is derived. Justi cation of the use of the nonparametric bootstrap in this context is sketched. The proposed estimators are extended to a working independence analysis of longitudinal/clustered data and applied to analyze an AIDS clinical trial dataset. The results of a simulation experiment are also given to illustrate our approach.