Streamlined solutions to multilevel sparse matrix problems

Tui H. Nolan; Matt P. Wand

doi:10.21914/anziamj.v62i0.14621

Authors

Tui H. Nolan University of Technology Sydney
Matt P. Wand University of Technology Sydney http://orcid.org/0000-0003-2555-896X

DOI:

https://doi.org/10.21914/anziamj.v62i0.14621

Keywords:

best linear unbiased prediction, linear mixed models, longitudinal data analysis, panel data, small-area estimation, variational inference

Abstract

We define and solve classes of sparse matrix problems that arise in multilevel modelling and data analysis. The classes are indexed by the number of nested units, with two-level problems corresponding to the common situation, in which data on level-1 units are grouped within a two-level structure. We provide full solutions for two-level and three-level problems, and their derivations provide blueprints for the challenging, albeit rarer in applications, higher-level versions of the problem. While our linear system solutions are a concise recasting of existing results, our matrix inverse sub-block results are novel and facilitate streamlined computation of standard errors in frequentist inference as well as allowing streamlined mean field variational Bayesian inference for models containing higher-level random effects.

doi: 10.1017/S1446181120000061

Author Biographies

Tui H. Nolan, University of Technology Sydney

University of Technology Sydney, P.O. Box 123, Broadway, New South Wales 2007, Australia.

Matt P. Wand, University of Technology Sydney