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Improved Large-Sample Confidence Intervals for Ratios of Variance Components in Nonnormal Distributions

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  • Brent D. Burch

Abstract

The confidence level of the usual interval for a ratio of variance components is contingent on normally distributed random variables. In this article we focus on confidence intervals for ratios of variance components in balanced one-way random effects models based on the large-sample properties of restricted maximum likelihood (REML) estimators. While this procedure does not require that the random variables be normally distributed, one must estimate a parameter that is a function of the kurtosis of the underlying distributions. Simulation results indicate that REML-based confidence interval methods outperform other well-known methods in the majority of the cases considered.

Suggested Citation

  • Brent D. Burch, 2015. "Improved Large-Sample Confidence Intervals for Ratios of Variance Components in Nonnormal Distributions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(2), pages 349-362, January.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:2:p:349-362
    DOI: 10.1080/03610926.2012.743567
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