The Annals of Statistics
- Ann. Statist.
- Volume 47, Number 5 (2019), 2855-2886.
Eigenvalue distributions of variance components estimators in high-dimensional random effects models
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.
Ann. Statist., Volume 47, Number 5 (2019), 2855-2886.
Received: November 2017
Revised: August 2018
First available in Project Euclid: 3 August 2019
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 62E20: Asymptotic distribution theory
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
- 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.