Fast Univariate Inference for Longitudinal Functional Models

[1]  D. Degras,et al.  Simultaneous confidence bands for nonparametric regression with functional data , 2009, 0908.1980.

[2]  Jeffrey S. Morris,et al.  Wavelet‐based functional mixed models , 2006, Journal of the Royal Statistical Society. Series B, Statistical methodology.

[3]  Torsten Hothorn,et al.  The functional linear array model , 2015 .

[4]  C. Matthews,et al.  Association of Sedentary Time with Mortality Independent of Moderate to Vigorous Physical Activity , 2012, PloS one.

[5]  Vadim Zipunnikov,et al.  Organizing and Analyzing the Activity Data in NHANES , 2019, Statistics in Biosciences.

[6]  Ana-Maria Staicu,et al.  Bootstrap‐based inference on the difference in the means of two correlated functional processes , 2012, Statistics in medicine.

[7]  Wensheng Guo Functional Mixed Effects Models , 2002 .

[8]  C. Morris Parametric Empirical Bayes Inference: Theory and Applications , 1983 .

[9]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[10]  Jacek Urbanek,et al.  The predictive performance of objective measures of physical activity derived from accelerometry data for 5-year all-cause mortality in older adults: NHANES 2003-2006. , 2019, The journals of gerontology. Series A, Biological sciences and medical sciences.

[11]  Dennis D. Cox,et al.  Pointwise testing with functional data using the Westfall–Young randomization method , 2008 .

[12]  S. Colcombe,et al.  Pointwise influence matrices for functional‐response regression , 2017, Biometrics.

[13]  Ciprian M Crainiceanu,et al.  Longitudinal penalized functional regression for cognitive outcomes on neuronal tract measurements , 2012, Journal of the Royal Statistical Society. Series C, Applied statistics.

[14]  David Ruppert,et al.  Semiparametric Regression: Author Index , 2003 .

[15]  Ana-Maria Staicu,et al.  Functional Additive Mixed Models , 2012, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.

[16]  Ricardo Fraiman,et al.  On the use of the bootstrap for estimating functions with functional data , 2006, Comput. Stat. Data Anal..

[17]  Jianqing Fan,et al.  Two‐step estimation of functional linear models with applications to longitudinal data , 1999 .

[18]  H. Müller,et al.  Functional Data Analysis for Sparse Longitudinal Data , 2005 .

[19]  So Young Park,et al.  Simple fixed‐effects inference for complex functional models , 2016, Biostatistics.

[20]  Raymond J. Carroll,et al.  A SIMULTANEOUS CONFIDENCE BAND FOR SPARSE LONGITUDINAL REGRESSION , 2012 .

[21]  Martin Styner,et al.  FMEM: Functional mixed effects modeling for the analysis of longitudinal white matter Tract data , 2014, NeuroImage.

[22]  H. Müller,et al.  Shrinkage Estimation for Functional Principal Component Scores with Application to the Population Kinetics of Plasma Folate , 2003, Biometrics.

[23]  Hongxiao Zhu,et al.  Robust, Adaptive Functional Regression in Functional Mixed Model Framework , 2011, Journal of the American Statistical Association.

[24]  Brian Caffo,et al.  Longitudinal High-Dimensional Principal Components Analysis with Application to Diffusion Tensor Imaging of Multiple Sclerosis. , 2015, The annals of applied statistics.

[25]  Lijian Yang,et al.  Simultaneous inference for the mean function based on dense functional data , 2012, Journal of nonparametric statistics.

[26]  Brian Caffo,et al.  Longitudinal functional principal component analysis. , 2010, Electronic journal of statistics.

[27]  Luo Xiao,et al.  Fast bivariate P‐splines: the sandwich smoother , 2013 .

[28]  Ciprian M Crainiceanu,et al.  Penalized Functional Regression , 2011, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.

[29]  Jeffrey S. Morris,et al.  Journal of the American Statistical Association Using Wavelet-based Functional Mixed Models to Characterize Population Heterogeneity in Accelerometer Profiles Using Wavelet-based Functional Mixed Models to Characterize Population Heterogeneity in Accelerometer Profiles: a Case Study , 2022 .

[30]  Ciprian M Crainiceanu,et al.  Structured functional principal component analysis , 2013, Biometrics.

[31]  Fabian Scheipl,et al.  Generalized Functional Additive Mixed Models , 2015, 1506.05384.

[32]  Jane-Ling Wang,et al.  Statistica Sinica Preprint No : SS-2017-0505 Title FMEM : Functional Mixed Effects Models for Longitudinal Functional Responses , 2018 .

[33]  C. Crainiceanu,et al.  Additive Functional Cox Model , 2020, Journal of Computational And Graphical Statistics.

[34]  J. Schrack,et al.  Generalized multilevel function‐on‐scalar regression and principal component analysis , 2015, Biometrics.

[35]  H. Müller,et al.  Modelling sparse generalized longitudinal observations with latent Gaussian processes , 2008 .

[36]  V. Zipunnikov,et al.  Re-evaluating the effect of age on physical activity over the lifespan. , 2017, Preventive medicine.

[37]  Jeffrey S. Morris,et al.  Functional CAR Models for Large Spatially Correlated Functional Datasets , 2016, Journal of the American Statistical Association.

[38]  P. Freedson,et al.  Amount of time spent in sedentary behaviors in the United States, 2003-2004. , 2008, American journal of epidemiology.