251 citations · 487 across the 35 of their papers we have counts for
8 papers · 1 filter
Differential Privacy Over Riemannian Manifolds
Matthew Reimherr, Karthik Bharath, Carlos Soto
In this work we consider the problem of releasing a differentially private statistical summary that resides on a Riemannian manifold. We present an extension of the Laplace or K-no…
Optimal Function-on-Scalar Regression over Complex Domains
Matthew Reimherr, Bharath Sriperumbudur, Hyun Bin Kang
In this work we consider the problem of estimating function-on-scalar regression models when the functions are observed over multi-dimensional or manifold domains and with potentia…
Testing Separability of Functional Time Series
Panayiotis Constantinou, Piotr Kokoszka, Matthew Reimherr
We derive and study a significance test for determining if a panel of functional time series is separable. In the context of this paper, separability means that the covariance stru…
Formal Privacy for Functional Data with Gaussian Perturbations
Ardalan Mirshani, Matthew Reimherr, Aleksandra Slavkovic
Motivated by the rapid rise in statistical tools in Functional Data Analysis, we consider the Gaussian mechanism for achieving differential privacy with parameter estimates taking…
Optimal Prediction for Additive Function-on-Function Regression
Matthew Reimherr, Bharath Sriperumbudur, Bahaeddine Taoufik
As with classic statistics, functional regression models are invaluable in the analysis of functional data. While there are now extensive tools with accompanying theory available f…
High-Dimensional Adaptive Function-on-Scalar Regression
Zhaohu Fan, Matthew Reimherr
Applications of functional data with large numbers of predictors have grown precipitously in recent years, driven, in part, by rapid advances in genotyping technologies. Given the…
The Function-on-Scalar LASSO with Applications to Longitudinal GWAS
Rina Foygel Barber, Matthew Reimherr, Thomas Schill
We present a new methodology for simultaneous variable selection and parameter estimation in function-on-scalar regression with an ultra-high dimensional predictor vector. We exten…
Break detection in the covariance structure of multivariate time series models
Alexander Aue, Siegfried Hörmann, Lajos Horváth +1
In this paper, we introduce an asymptotic test procedure to assess the stability of volatilities and cross-volatilites of linear and nonlinear multivariate time series models. The…