Notes on asymptotics of sample eigenstructure for spiked covariance models with non-Gaussian data

10/24/2018
by   Iain M. Johnstone, et al.
0

These expository notes serve as a reference for an accompanying post Morales-Jimenez et al. [2018]. In the spiked covariance model, we develop results on asymptotic normality of sample leading eigenvalues and certain projections of the corresponding sample eigenvectors. The results parallel those of Paul [2007], but are given using the non-Gaussian model of Bai and Yao [2008]. The results are not new, and citations are given, but proofs are collected and organized as a point of departure for Morales-Jimenez et al. [2018].

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/23/2021

Gaussian and Hermite Ornstein-Uhlenbeck processes

In the present paper we study the asymptotic behavior of the auto-covari...
research
11/14/2019

An Invariant Test for Equality of Two Large Scale Covariance Matrices

In this work, we are motivated by the recent work of Zhang et al. (2019)...
research
07/24/2019

Notes on Latent Structure Models and SPIGOT

These notes aim to shed light on the recently proposed structured projec...
research
07/09/2021

Darlin: A proof carrying data scheme based on Marlin

In this document we describe the Darlin proof carrying data scheme for t...
research
01/20/2014

A Scalable Conditional Independence Test for Nonlinear, Non-Gaussian Data

Many relations of scientific interest are nonlinear, and even in linear ...
research
06/15/2023

Fit Like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Markov Chains

Score matching is an approach to learning probability distributions para...

Please sign up or login with your details

Forgot password? Click here to reset