EconPapers    
Economics at your fingertips  
 

On the Robustness of Bayes Estimators of the Variance Ratio in Balanced One-Way ANOVA Models with Covariates

Malay Ghosh and Robert M. Baskin

Chapter 18 in Statistical Theory and Applications, 1996, pp 217-231 from Springer

Abstract: Abstract This paper introduces some hierarchical Bayes (HB) estimators of the variance ratio in balanced one-way ANOVA models with covariates. Such estimators enjoy frequentist properties like consistency and asymptotic normality. Jackknifed estimators of the asymptotic variance of the HB estimators are found, and are used in the construction of asymptotic confidence intervals for the variance ratio. These intervals have larger coverage probability than similar intervals based on the maximum likelihood estimators, restricted maximum likelihood estimators, and estimators based on the Henderson-III method. The HB intervals are also much more robust than the competing intervals when the underlying distributions are double exponential or uniform.

Keywords: One-way ANOVA; variance ratio; hierarchical Bayes; robustness; asymptotic properties; jackknife; maximum likelihood (search for similar items in EconPapers)
Date: 1996
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:sprchp:978-1-4612-3990-1_18

Ordering information: This item can be ordered from
http://www.springer.com/9781461239901

DOI: 10.1007/978-1-4612-3990-1_18

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-12-11
Handle: RePEc:spr:sprchp:978-1-4612-3990-1_18