research
∙
12/09/2022
A note on the prediction error of principal component regression in high dimensions
We analyze the prediction error of principal component regression (PCR) ...
share
research
∙
11/04/2022
Optimal parameter estimation for linear SPDEs from multiple measurements
The coefficients in a general second order linear stochastic partial dif...
research
∙
08/26/2022
Quantitative limit theorems and bootstrap approximations for empirical spectral projectors
Given finite i.i.d. samples in a Hilbert space with zero mean and trace-...
research
∙
07/31/2021
Functional estimation in log-concave location families
Let {P_θ:θ∈ℝ^d} be a log-concave location family with P_θ(dx)=e^-V(x-θ)d...
research
∙
07/19/2021
Van Trees inequality, group equivariance, and estimation of principal subspaces
We establish non-asymptotic lower bounds for the estimation of principal...
research
∙
11/15/2020
Nonparametric goodness-of-fit testing for parametric covariate models in pharmacometric analyses
The characterization of covariate effects on model parameters is a cruci...
research
∙
05/14/2020
Information inequalities for the estimation of principal components
We provide lower bounds for the estimation of the eigenspaces of a covar...
research
∙
04/17/2020
Analyzing the discrepancy principle for kernelized spectral filter learning algorithms
We investigate the construction of early stopping rules in the nonparame...
research
∙
10/18/2019
On the perturbation series for eigenvalues and eigenprojections
A standard perturbation result states that perturbed eigenvalues and eig...
research
∙
09/24/2019
High-probability bounds for the reconstruction error of PCA
We identify principal component analysis (PCA) as an empirical risk mini...
research
∙
11/07/2018