Envelopes for multivariate linear regression with linearly constrained coefficients

TL;DR

This paper develops and compares envelope methods for constrained multivariate linear regression models, proposing new estimators and tests to improve efficiency and bias correction, supported by simulations and real data applications.

Contribution

It introduces a novel envelope estimator for constrained multivariate linear models and develops envelope-based testing methods, filling a gap in existing methodology.

Findings

The constrained envelope estimator improves efficiency over standard methods.

Simulation studies demonstrate reduced bias and variance.

Applications to real datasets validate the practical utility.

Abstract

A constrained multivariate linear model is a multivariate linear model with the columns of its coefficient matrix constrained to lie in a known subspace. This class of models includes those typically used to study growth curves and longitudinal data. Envelope methods have been proposed to improve estimation efficiency in the class of unconstrained multivariate linear models, but have not yet been developed for constrained models that we develop in this article. We first compare the standard envelope estimator based on an unconstrained multivariate model with the standard estimator arising from a constrained multivariate model in terms of bias and efficiency. Then, to further improve efficiency, we propose a novel envelope estimator based on a constrained multivariate model. Novel envelope-based testing methods are also proposed. We provide support for our proposals by simulations and by studying the classical dental data and data from the China Health and Nutrition Survey and a study of probiotic capacity to reduced Salmonella infection.