Ben Deitmar

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20222026

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math.ST2026

Spectral approximation for the separable covariance mixture model

Ben Deitmar

This paper introduces the separable covariance mixture model, which assumes a data-matrix $Y$ to be of the form $r = 1 \sum R A_{r} X B_{r}$ for one random $(d \times n)$ -ma…

math.ST2025

Estimation of Population Linear Spectral Statistics by Marchenko--Pastur Inversion

Ben Deitmar

A new method of estimating population linear spectral statistics from high-dimensional data is introduced. When the dimension $d$ grows with the sample size $n$ such that $\frac{d}…

math.ST2024

Marchenko-Pastur laws for Daniell smoothed periodograms

Ben Deitmar

Given a sample $X_{0}, ..., X_{n - 1}$ from a $d$ -dimensional stationary time series $(X_{t})_{t \in Z}$ , the most commonly used estimator for the spectral density matrix $F (θ)$ a…

math.ST2022

Trace Moments of the Sample Covariance Matrix with Graph-Coloring

Ben Deitmar

Let $S_{p, n}$ denote the sample covariance matrix based on $n$ independent identically distributed $p$ -dimensional random vectors in the null-case. The main result of this paper is…