Reviews

Functional Data Analysis (Brief Overview) (Encyclopedia Version) (Print Version)

Wang, J.L., Chiou, J., Müller, H.G. (2016). Functional Data Analysis. Annual Review of Statistics and Its Application 3 257-295. (pdf)

Müller, H.G., Petersen, A. (2016). Density estimation including examples. Wiley StatsRef: Statistics Reference Online (WSR) (pdf)

Müller, H.G. (2016). Peter Hall, functional data analysis and random objects. Annals of Statistics 44, 1867-1887. (pdf)

Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017). Quantifying infinite-dimensional data: Functional Data Analysis in action. Statistics in Biosciences 9, 582-604. (pdf)

Müller, H.G. (2009). Functional modeling of longitudinal data. In: Longitudinal Data Analysis (Handbooks of Modern Statistical Methods), Ed. Fitzmaurice, G., Davidian, M., Verbeke, G., Molenberghs, G., Wiley, New York, 223--252. (pdf)

Preprints and Recent Papers

Carroll, C., Bhattacharjee, S., Chen, Y., Dubey, P., Fan, J. Gajardo, A., Zhou, X., Müller, H.G., Wang, J.L. (2020). Time dynamics of COVID-19. Scientific Reports 10:21040 (paper) (preprint) (supplement)

Dubey, P., Müller, H.G. (2020). Functional models for time-varying random objects. J Royal Statistical Society B 82, 275-327. (paper and discussion) (preprint) (movies)

Chen, Y., Lin, Z., Müller, H.G. (2020). Wasserstein Regression. (pdf)

Carroll, C., Müller, H.G., Kneip, A. (2020). Cross-component registration for multivariate functional data, with application to growth curves. Biometrics (pdf) (supplement)

Lin, Z., Lopes, M.E., Müller, H.G. (2020). High-dimensional MANOVA via bootstrapping and its application to functional data and sparse count data. (pdf)

Chen, Y., Müller, H.G. (2020). Uniform convergence of local Frechet regression, with applications to locating extrema and time warping for metric-space valued trajectories. (pdf)

Dai, X., Lin, Z., Müller, H.G. (2020). Modeling sparse longitudinal data on Riemannian manifolds. Biometrics (pdf) (supplement)

Lin, Z., Müller, H.G., Park, B.U. (2020). Additive models for symmetric positive-definite matrices, Riemannian manifolds and Lie groups. (pdf)

Gajardo, A., Müller, H.G. (2020). Point process regression. (pdf)

Chen, Y., Dawson, M., Müller, H.G. (2020). Rank dynamics for functional data. Computational Statistics and Data Analysis, 106963 (pdf)

Lopes, M.E., Lin, Z., Müller, H.G. (2020). Bootstrapping max statistics in high dimensions: Near-parametric rates under weak variance decay and application to functional data analysis. Annals of Statistics (pdf)

Han, K., Müller, H.G., Park, B. (2020). Additive functional regression for densities as responses. J. American Statistical Association (pdf)

Dubey, P., Müller, H.G. (2020). Fréchet change-point detection. Annals of Statistics (pdf) (supplement)

Lin, Z., Müller, H.G. (2019). Total variation regularized Fréchet regression for metric-space valued data. (pdf)

Petersen, A., Müller, H.G. (2019) Discussion of: A Spatial Modeling Approach for Linguistic Object Data: Analyzing Dialect Sound Variations Across Great Britain, by Shahin Tavakoli et al. J. American Statistical Association (pdf)

Petersen, A., Müller, H.G. (2019). Fréchet regression for random objects with Euclidean predictors. Annals of Statistics (pdf) (supplement)

Petersen, A., Deoni, S., Müller, H.G. (2019). Fréchet estimation of time-varying covariance matrices from sparse data, with application to the regional co-evolution of myelination in the developing brain. Annals of Applied Statistics (pdf) (supplement)

Petersen, A., Müller, H.G. (2019). Wasserstein covariance for multiple random densities. Biometrika (pdf) (supplement)

Dai, X., Müller, H.G., Wang, J.L., Deoni, S. (2019). Age-dynamic networks and functional correlation for early white matter myelination. Brain Structure and Function (pdf)

Petersen, A., Chen, C.J., Müller, H.G. (2019). Quantifying and visualizing intraregional connectivity in resting-state functional Magnetic Resonance Imaging with correlation densities. Brain Connectivity (pdf)

Dubey, P, Müller, H.G. (2019). Fréchet analysis of variance for random objects. Biometrika (pdf) (supplement)