Asymptotic spectrum of weighted sample covariance: another proof of spectrum convergence

Fuente: arXiv
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Main Author: Oriol, Benoit
Format: Preprint
Published: 2024
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author Oriol, Benoit
author_facet Oriol, Benoit
contents We propose another proof of the high dimensional spectrum convergence of the weighted sample covariance, more concise and self-sufficient but with stronger, but reasonable assumptions. We explain and illustrates this theorem for different weight distributions and show how the spectrum behaves in finite samples with heavy tails. The general purpose is to provide a detailed introduction to the high dimensional spectrum of weighted sample covariance.
format Preprint
id arxiv_https___arxiv_org_abs_2410_14408
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Asymptotic spectrum of weighted sample covariance: another proof of spectrum convergence
Oriol, Benoit
Statistics Theory
Probability
Machine Learning
We propose another proof of the high dimensional spectrum convergence of the weighted sample covariance, more concise and self-sufficient but with stronger, but reasonable assumptions. We explain and illustrates this theorem for different weight distributions and show how the spectrum behaves in finite samples with heavy tails. The general purpose is to provide a detailed introduction to the high dimensional spectrum of weighted sample covariance.
title Asymptotic spectrum of weighted sample covariance: another proof of spectrum convergence
topic Statistics Theory
Probability
Machine Learning
url https://arxiv.org/abs/2410.14408