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Sharp bounds under contaminated or corrupted sampling with verification, with an application to environmental pollutant data

Dominitz, Jeff and Sherman, Robert P. (2004) Sharp bounds under contaminated or corrupted sampling with verification, with an application to environmental pollutant data. Journal of Agricultural, Biological, and Environmental Statistics, 9 (3). pp. 319-338. ISSN 1085-7117. https://resolver.caltech.edu/CaltechAUTHORS:20200204-084737505

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Abstract

Let F denote a distribution of interest and G a possibly spurious distribution. This article derives and nonparametrically estimates sharp bounds on characteristics of F when the data are a mixture of F and G, and a fraction of the data is verified to be from F. Contaminated and corrupted mixtures, with and without monotonicity restrictions, are analyzed. The methods are particularly useful in analyzing environmental pollutant measurements obtained using gas chromatography-mass spectroscopy. Results are applied to measurements of organic pollutant concentrations from the Love Canal. We argue that a corruption with monotonic verification model may be the most appropriate model for this type of data.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1198/108571104x3389DOIArticle
https://rdcu.be/b1b9mPublisherFree ReadCube access
Additional Information:© 2004 American Statistical Association and the International Biometric Society. Received 15 July 2003; Revised 15 December 2003. We thank Diane Lambert for providing the organic pollutant data analyzed in this article. We also thank Nathan Dalleska, Director of the Environmental Analysis Center at the California Institute of Technology, for sharing his insights about gas chromatography-mass spectroscopy.
Subject Keywords:Gas chromatography-mass spectroscopy; Mixture models
Issue or Number:3
Record Number:CaltechAUTHORS:20200204-084737505
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200204-084737505
Official Citation:Dominitz, J. & Sherman, R.P. JABES (2004) 9: 319. https://doi.org/10.1198/108571104X3389
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:101104
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:04 Feb 2020 17:15
Last Modified:04 Feb 2020 17:15

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