Journal article
Bayesian credible intervals for monitoring liquid blending rates
Model assisted statistics and applications, Vol.6(2), pp.75-80
05/05/2011
DOI: 10.3233/MAS-2011-0175
Abstract
We consider the problem of constructing confidence intervals (CIs) for the blending coefficient of different liquid, such as the blended underground storage tank (UST) leak data for compliance. For this problem, confidence intervals based on Fieller's Method have been proposed. This method utilizes a blending coefficient estimator which is a ratio of two correlated normal random variables. However, this method assumes normally distributed random errors in the UST leak model and therefore may be inappropriate for the UST leak data which typically have heavy-tailed empirical distributions. In this paper we develop a Bayesian approach assuming non-normal random errors with the Power Exponential Distribution (PED). A real-data example using Cary blended site data is given to illustrate both the Fieller's CIs and the Bayesian credible intervals. Monte Carlo simulations are conducted to compare the coverage probability and average width of CIs for both methods. For data with heavy-tailed distributions, the simulations show that both Fieller's and Bayesian intervals perform adequately in terms of coverage. However, Bayesian intervals perform better in terms of yielding CIs with shorter expected width. © 2011 - IOS Press and the authors. All rights reserved.
Details
- Title: Subtitle
- Bayesian credible intervals for monitoring liquid blending rates
- Creators
- Dewi Rahardja - The University of Texas Southwestern Medical CenterYan D. Zhao - The University of Texas Southwestern Medical CenterXian-Jin Xie - The University of Texas Southwestern Medical Center
- Resource Type
- Journal article
- Publication Details
- Model assisted statistics and applications, Vol.6(2), pp.75-80
- DOI
- 10.3233/MAS-2011-0175
- ISSN
- 1574-1699
- eISSN
- 1875-9068
- Language
- English
- Date published
- 05/05/2011
- Academic Unit
- Preventive and Community Dentistry; Biostatistics; Dental Research
- Record Identifier
- 9984367654302771
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