Estimating Signal-to-Noise Ratios for Multivariate High-dimensional Linear Models

Abstract

Signal-to-noise ratios (SNR) play a crucial role in various statistical models, with important applications in tasks such as estimating heritability in genomics. The method-of-moments estimator is a widely used approach for estimating SNR, primarily explored in single-response settings. In this study, we extend the method-of-moments SNR estimation framework to encompass both fixed effects and random effects linear models with multivariate responses. In particular, we establish and compare the asymptotic distributions of the proposed estimators. Furthermore, we extend our approach to accommodate cases with residual heteroskedasticity and derive asymptotic inference procedures based on standard error estimation. The effectiveness of our methods is demonstrated through extensive numerical experiments.