Assessing the robustness of estimators when fitting Poisson inverse Gaussian models

Kimberly S. Weems () and Paul J. Smith ()
Additional contact information
Kimberly S. Weems: North Carolina Central University
Paul J. Smith: University of Maryland

Metrika: International Journal for Theoretical and Applied Statistics, 2018, vol. 81, issue 8, No 4, 985-1004

Abstract: Abstract The generalized linear mixed model (GLMM) extends classical regression analysis to non-normal, correlated response data. Because inference for GLMMs can be computationally difficult, simplifying distributional assumptions are often made. We focus on the robustness of estimators when a main component of the model, the random effects distribution, is misspecified. Results for the maximum likelihood estimators of the Poisson inverse Gaussian model are presented.

Keywords: Poisson mixed models; Inverse Gaussian distribution; Influence function; Directional derivative; Maximum likelihood estimation (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00184-018-0664-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:metrik:v:81:y:2018:i:8:d:10.1007_s00184-018-0664-1

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/184/PS2

DOI: 10.1007/s00184-018-0664-1

Access Statistics for this article

Metrika: International Journal for Theoretical and Applied Statistics is currently edited by U. Kamps and Norbert Henze

More articles in Metrika: International Journal for Theoretical and Applied Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().