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13,521
Longitudinal data analysis using generalized linear models”.
- Biometrika,
, 1986
"... SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating ..."
Abstract
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Cited by 1526 (8 self)
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SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence
Quantile regression for longitudinal data
- Journal of Multivariate Analysis
, 2004
"... Abstract. The penalized least squares interpretation of the classical random ef-fects estimator suggests a possible way forward for quantile regression models with a large number of “fixed effects”. The introduction of a large number of individual fixed effects can significantly inflate the variabil ..."
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Cited by 125 (2 self)
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the variability of estimates of other covariate effects. Regularization, or shrinkage of these individual effects toward a common value can help to mollify this inflation effect. A general approach to estimating quantile regression models for longitudinal data is proposed employing `1 regular-ization methods
longitudinal data
, 1995
"... The aim of this paper is to simultaneously estimate n curves corrupted by noise, this means sev-eral observations of a random process. The non-parametric estimation of the sampled paths leads to a new kind of functional principal components analysis which simultaneously takes into account a dimensio ..."
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dimensionality and a smoothness constraint. Furthermore, the use of B-spline approximation to esti-mate the curves allows the study of unbalanced longitudinal data. The relationship between the choice of the smoothing parameter and that of dimensional&y is discussed. A simulation study shows good behaviors
G: Models for Discrete Longitudinal Data
- and Chen 6 © 2012 by American Society of Clinical Oncology JOURNAL OF CLINICAL ONCOLOGY
"... This book covers a wide variety of statistical techniques for longitudinal data analysis. The authors, Geert Molenberghs and Geert Verbeke –both well known in this field – have extended their previous textbook (Verbeke and Molenberghs, 1997), mainly focused on linear mixed model for continuous data, ..."
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Cited by 172 (16 self)
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This book covers a wide variety of statistical techniques for longitudinal data analysis. The authors, Geert Molenberghs and Geert Verbeke –both well known in this field – have extended their previous textbook (Verbeke and Molenberghs, 1997), mainly focused on linear mixed model for continuous data
Functional Modeling of Longitudinal Data
, 2006
"... Functional data analysis provides an inherently nonparametric approach for the analysis of data which consist of samples of time courses or random trajectories. It is a relatively young field aiming at modeling and data exploration under very flexible model assumptions with no or few parametric comp ..."
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Cited by 3 (0 self)
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as continuously observed, in longitudinal data analysis one mostly deals with sparsely and irregularly observed data that also are corrupted with noise. Adjustments of functional data analysis techniques which take these particular features into account are needed to use them to advantage for longitudinal data
Functional data analysis for sparse longitudinal data.
- Journal of the American Statistical Association
, 2005
"... 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 a ..."
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Cited by 123 (24 self)
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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
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