Estimation and Inference for Very Large Linear Mixed Effects Models

Gao, Katelyn; Owen, Arthur B

doi:10.48550/arxiv.1610.08088

Search citation statements

Order By: Relevance

Paper Sections

Select...

Crossed Random Effects Models1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

Publication Types

Select...

Other1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This has been a great challenge for large data sets. For example, Gao and Owen [3] showed that evaluating the likelihood of the model once already has complexity at least of order N 3 2 when there are N observations. The lmer function in R package lme4 [1] has a cost that grows like N 3/2 and Bates et al [1] removed the MCMC option from the package because it was considered unreliable.…”

Section: Crossed Random Effects Modelsmentioning

confidence: 99%

Convergence rate of a collapsed Gibbs sampler for crossed random effects models

Ghosh¹,

Zhong²

2021

Preprint

View full text Add to dashboard Cite

In this paper, we analyze the convergence rate of a collapsed Gibbs sampler for crossed random effects models. Our results apply to a substantially larger range of models than previous works, including models that incorporate missingness mechanism and unbalanced level data. The theoretical tools involved in our analysis include a connection between relaxation time and autoregression matrix, concentration inequalities, and random matrix theory.

show abstract