We propose method-of-moments estimators for the eigenvalues of variance component covariance matrices in multivariate mixed effects models. Assuming a parametric form for the eigenvalue distribution, we focus on the high-dimensional regime where the number of predictors is large and comparable to the number of realizations of each random effect. In this setting, we show that the empirical moments of sum-of-squares matrices (e.g., MANOVA estimators of the covariance matrices) can be closely approximated by deterministic functions of the underlying parameters. This relationship enables the construction of consistent and asymptotically normal estimators via moment matching. Our approach is motivated by applications in quantitative genetics, where estimating genetic covariance components across multiple phenotypic traits is of central interest. We implement our method in a new python package mlmm-mom, and demonstrate how our method adapts to several common experimental designs in this domain.
Abstract We propose a new two-sample test for high-dimensional covariance matrices under elliptical distributions. The unbiased and consistent estimators of the functions of covariance matrices are proposed in high-dimensional elliptical models. This allows us to introduce a modified statistic that accounts for these p...
Jie-Bin Wu, Nan An, Long-Xiang Fang et al.· Communications in Statistics...· 0 citations
Linear mixed models are often used to analyze studies with repeated measurements over time, where the data present a correlation between observations of the same individual. However, one of the main challenges in using these models is correctly defining the covariance structure associated with error and random effects....
Keyliane Travassos, L. A. Matos, Fernanda L. Schumacher· Statistics in Medicine· 0 citations
The analysis of large data panels is important in econometrics and beyond. Prediction and inference methods for such data typically rely on simplifying model assumptions for the covariance structure of errors. One convenient assumption is what we call groupwise sphericity: that errors are uncorrelated across individual...
Daria Tieplova, Nina Dörnemann, Tim Kutta· 0 citations
We study the asymptotic properties of the nonlinear stochastic GMM algorithm proposed by Chen, Lee, Liao, Seo, Shin and Song for recursively estimating parameters defined by possibly nonlinear and overidentified moment restrictions $\E[g(X,\beta^*)]=0$. Each observation updates estimates of the moment Jacobian, inverse...
In the context of functional data analysis, we propose a Bayesian test to assess whether the mean function of a Gaussian population is identically zero. By leveraging the structure of the reproducing kernel Hilbert space (RKHS) associated with the covariance function of the underlying process, we approximate the mean b...
J. R. Berrendero, A. Coín, Antonio Cuevas· 0 citations
This work proposes computationally efficient tests for equality of mean vectors of two or more high-dimensional populations by establishing an equivalence between equality of means and a zero population logistic regression parameter.
Sayan Das, Debraj Das, S. Dutta· 0 citations
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