Advances in Decision Sciences
Volume 2011 (2011), Article ID 571768, 8 pages
Research Article

Modelling Inverse Gaussian Data with Censored Response Values: EM versus MCMC

1CSIRO Mathematical, Informatics, and Statistics, Locked Bag 17, North Ryde, NSW 1670, Australia
2School of Mathematics and Statatistics, University of Sydney, Camperdown, NSW 2006, Australia

Received 8 September 2010; Revised 13 January 2011; Accepted 4 April 2011

Academic Editor: Shelton Peiris

Copyright © 2011 R. S. Sparks et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Low detection limits are common in measure environmental variables. Building models using data containing low or high detection limits without adjusting for the censoring produces biased models. This paper offers approaches to estimate an inverse Gaussian distribution when some of the data used are censored because of low or high detection limits. Adjustments for the censoring can be made if there is between 2% and 20% censoring using either the EM algorithm or MCMC. This paper compares these approaches.