Distributional outcome regression and its application to modelling continuously monitored heart rate and physical activity

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[1]  J. Schrack,et al.  Shape-constrained estimation in functional regression with Bernstein polynomials , 2022, Comput. Stat. Data Anal..

[2]  N. Punjabi,et al.  A case study of glucose levels during sleep using fast function on scalar regression inference , 2022, 2205.08439.

[3]  Ciprian M. Crainiceanu,et al.  Fast Univariate Inference for Longitudinal Functional Models , 2021, J. Comput. Graph. Stat..

[4]  V. Zipunnikov,et al.  Scalar on time-by-distribution regression and its application for modelling associations between daily-living physical activity and cognitive functions in Alzheimer’s Disease , 2021, Scientific Reports.

[5]  Victor M. Panaretos,et al.  Distribution-on-Distribution Regression via Optimal Transport Maps , 2021, Biometrika.

[6]  Matteo Pegoraro,et al.  Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric , 2021, J. Mach. Learn. Res..

[7]  Piotr Kokoszka,et al.  Modeling Probability Density Functions as Data Objects , 2021 .

[8]  K. Hron,et al.  Compositional Scalar-on-Function Regression with Application to Sediment Particle Size Distributions , 2021, Mathematical Geosciences.

[9]  Jeffrey M. Hausdorff,et al.  Distributional data analysis via quantile functions and its application to modeling digital biomarkers of gait in Alzheimer's Disease. , 2021, Biostatistics.

[10]  Marcos Matabuena,et al.  Glucodensities: A new representation of glucose profiles using distributional data analysis , 2020, Statistical methods in medical research.

[11]  H. Müller,et al.  Wasserstein Regression , 2020, Journal of the American Statistical Association.

[12]  A. Maity,et al.  A Score Based Test for Functional Linear Concurrent Regression , 2018, Econometrics and Statistics.

[13]  M. Matabuena,et al.  Distributional data analysis with accelerometer data in a NHANES database with nonparametric survey regression models , 2021 .

[14]  Martin A Lindquist,et al.  Differences in functional connectivity distribution after transcranial direct‐current stimulation: A connectivity density point of view , 2020, bioRxiv.

[15]  Hojin Yang.  Random distributional response model based on spline method , 2020 .

[16]  Jeffrey S. Morris,et al.  Quantile Function on Scalar Regression Analysis for Distributional Data , 2020, Journal of the American Statistical Association.

[17]  J. Mandrola,et al.  Relation of Obesity to New-Onset Atrial Fibrillation and Atrial Flutter in Adults. , 2018, The American journal of cardiology.

[18]  J. Faraway,et al.  Modelling a response as a function of high-frequency count data: The association between physical activity and fat mass , 2014, Statistical methods in medical research.

[19]  Peter Filzmoser,et al.  Simplicial principal component analysis for density functions in Bayes spaces , 2016, Comput. Stat. Data Anal..

[20]  H. Muller,et al.  Functional data analysis for density functions by transformation to a Hilbert space , 2016, 1601.02869.

[21]  Gareth M. James,et al.  Functional additive regression , 2015, 1510.04064.

[22]  Jeffrey S. Morris,et al.  Bayesian function‐on‐function regression for multilevel functional data , 2015, Biometrics.

[23]  K. Prabhavathi,et al.  Role of biological sex in normal cardiac function and in its disease outcome - a review. , 2014, Journal of clinical and diagnostic research : JCDR.

[24]  Antonio Irpino,et al.  A Metric Based Approach for the Least Square Regression of Multivariate Modal Symbolic Data , 2013, Statistical Models for Data Analysis.

[25]  J. Wang,et al.  Shape restricted nonparametric regression with Bernstein polynomials , 2012, Comput. Stat. Data Anal..

[26]  Hans-Georg Müller,et al.  Functional Data Analysis , 2016 .

[27]  Ci-Ren Jiang,et al.  Functional single index models for longitudinal data , 2011, 1103.1726.

[28]  Antonio Irpino,et al.  Ordinary Least Squares for Histogram Data Based on Wasserstein Distance , 2010, COMPSTAT.

[29]  Ronald L Gellish,et al.  Longitudinal modeling of the relationship between age and maximal heart rate. , 2007, Medicine and science in sports and exercise.

[30]  Jane-ling Wang,et al.  Functional linear regression analysis for longitudinal data , 2005, math/0603132.

[31]  Emanuel Parzen,et al.  Quantile Probability and Statistical Data Modeling , 2004 .

[32]  I. Antelmi,et al.  Influence of age, gender, body mass index, and functional capacity on heart rate variability in a cohort of subjects without heart disease. , 2004, The American journal of cardiology.

[33]  Jianhua Z. Huang,et al.  Polynomial Spline Estimation and Inference for Varying Coefficient Models with Longitudinal Data , 2003 .

[34]  P. Donnan,et al.  The morning surge in blood pressure and heart rate is dependent on levels of physical activity after waking , 2002, Journal of hypertension.

[35]  Jianhua Z. Huang,et al.  Varying‐coefficient models and basis function approximations for the analysis of repeated measurements , 2002 .

[36]  Hirofumi Tanaka,et al.  Age-predicted maximal heart rate revisited. , 2001, Journal of the American College of Cardiology.

[37]  Juan Manuel Peña,et al.  Shape preserving representations and optimality of the Bernstein basis , 1993, Adv. Comput. Math..

[38]  J. Ramsay.  Monotone Regression Splines in Action , 1988 .

[39]  Donald Goldfarb,et al.  A numerically stable dual method for solving strictly convex quadratic programs , 1983, Math. Program..

[40]  J. Kostis,et al.  The Effect of Age on Heart Rate in Subjects Free of Heart Disease: Studies By Ambulatory Electrocardiography and Maximal Exercise Stress Test , 1982, Circulation.

[41]  D. Goldfarb,et al.  Dual and primal-dual methods for solving strictly convex quadratic programs , 1982 .