720 citations · 1.5k across the 26 of their papers we have counts for
8 papers · 1 filter
Functional linear and single-index models: A unified approach via Gaussian Stein identity
Krishnakumar Balasubramanian, Hans-Georg Müller, Bharath K. Sriperumbudur
Functional linear and single-index models are core regression methods in functional data analysis and are widely used for performing regression in a wide range of applications when…
Fréchet Analysis Of Variance For Random Objects
Paromita Dubey, Hans-Georg Müller
Fréchet mean and variance provide a way of obtaining mean and variance for general metric space valued random variables and can be used for statistical analysis of data objects tha…
Principal Component Analysis for Functional Data on Riemannian Manifolds and Spheres
Xiongtao Dai, Hans-Georg Müller
Functional data analysis on nonlinear manifolds has drawn recent interest. Sphere-valued functional data, which are encountered for example as movement trajectories on the surface…
Functional data analysis for density functions by transformation to a Hilbert space
Alexander Petersen, Hans-Georg Müller
Functional data that are nonnegative and have a constrained integral can be considered as samples of one-dimensional density functions. Such data are ubiquitous. Due to the inheren…
Nonlinear manifold representations for functional data
Dong Chen, Hans-Georg Müller
For functional data lying on an unknown nonlinear low-dimensional space, we study manifold learning and introduce the notions of manifold mean, manifold modes of functional variati…
Properties of principal component methods for functional and longitudinal data analysis
Peter Hall, Hans-Georg Müller, Jane-Ling Wang
The use of principal component methods to analyze functional data is appropriate in a wide range of different settings. In studies of ``functional data analysis,'' it has often bee…
Inference for covariate adjusted regression via varying coefficient models
Damla Şentürk, Hans-Georg Müller
We consider covariate adjusted regression (CAR), a regression method for situations where predictors and response are observed after being distorted by a multiplicative factor. The…
Functional linear regression analysis for longitudinal data
Fang Yao, Hans-Georg Müller, Jane-Ling Wang
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 covariat…