The Annals of Statistics

Eigenvalue distributions of variance components estimators in high-dimensional random effects models

Zhou Fan and Iain M. Johnstone

Full-text: Open access

Abstract

We study the spectra of MANOVA estimators for variance component covariance matrices in multivariate random effects models. When the dimensionality of the observations is large and comparable to the number of realizations of each random effect, we show that the empirical spectra of such estimators are well approximated by deterministic laws. The Stieltjes transforms of these laws are characterized by systems of fixed-point equations, which are numerically solvable by a simple iterative procedure. Our proof uses operator-valued free probability theory, and we establish a general asymptotic freeness result for families of rectangular orthogonally invariant random matrices, which is of independent interest. Our work is motivated in part by the estimation of components of covariance between multiple phenotypic traits in quantitative genetics, and we specialize our results to common experimental designs that arise in this application.

Article information

Source
Ann. Statist., Volume 47, Number 5 (2019), 2855-2886.

Dates
Received: November 2017
Revised: August 2018
First available in Project Euclid: 3 August 2019

Permanent link to this document
https://projecteuclid.org/euclid.aos/1564797866

Digital Object Identifier
doi:10.1214/18-AOS1767

Mathematical Reviews number (MathSciNet)
MR3988775

Subjects
Primary: 62E20: Asymptotic distribution theory

Keywords
Random matrix theory free probability deterministic equivalents covariance estimation

Citation

Fan, Zhou; Johnstone, Iain M. Eigenvalue distributions of variance components estimators in high-dimensional random effects models. Ann. Statist. 47 (2019), no. 5, 2855--2886. doi:10.1214/18-AOS1767. https://projecteuclid.org/euclid.aos/1564797866


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Supplemental materials

  • Supplementary Appendices. The Appendices contain a discussion of more general classification designs, proofs of Theorem 3.10 and Corollary 3.11, the proof of Lemma 4.3 and the conclusion of the proof of Theorem 4.1 and a separate exposition of the proof in Section 4 for the simpler setting of Theorem 1.1.