The Annals of Statistics

Asymptotic normality and valid inference for Gaussian variational approximation

Peter Hall, Tung Pham, M. P. Wand, and S. S. J. Wang

Full-text: Open access

Abstract

We derive the precise asymptotic distributional behavior of Gaussian variational approximate estimators of the parameters in a single-predictor Poisson mixed model. These results are the deepest yet obtained concerning the statistical properties of a variational approximation method. Moreover, they give rise to asymptotically valid statistical inference. A simulation study demonstrates that Gaussian variational approximate confidence intervals possess good to excellent coverage properties, and have a similar precision to their exact likelihood counterparts.

Article information

Source
Ann. Statist. Volume 39, Number 5 (2011), 2502-2532.

Dates
First available in Project Euclid: 30 November 2011

Permanent link to this document
https://projecteuclid.org/euclid.aos/1322663466

Digital Object Identifier
doi:10.1214/11-AOS908

Mathematical Reviews number (MathSciNet)
MR2906876

Zentralblatt MATH identifier
1231.62029

Subjects
Primary: 62F12: Asymptotic properties of estimators
Secondary: 62F25: Tolerance and confidence regions

Keywords
Generalized linear mixed models longitudinal data analysis maximum likelihood estimation Poisson mixed models

Citation

Hall, Peter; Pham, Tung; Wand, M. P.; Wang, S. S. J. Asymptotic normality and valid inference for Gaussian variational approximation. Ann. Statist. 39 (2011), no. 5, 2502--2532. doi:10.1214/11-AOS908. https://projecteuclid.org/euclid.aos/1322663466


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