Fitting a second-order parametric distribution conditioned on an explanatory variable using maximum likelihood estimation

David E. Burmaster and Kimberly M. Thompson

Journal of Risk Research, 2001, vol. 4, issue 1, 49-62

Abstract: We show how to use the method of maximum likelihood estimation (MLE) to fit a second-order parametric distribution conditioned on a single explanatory variable to data. To illustrate the method, we demonstrate how a second-order log-normal distribution, conditioned on the population served, can model the variability and the parametric uncertainty in data collected by the US Environmental Protection Agency for the concentration of radon 222 in drinking water supplied from ground water, even though 28% of the data fall at or below the minimum reporting level.

Date: 2001
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/136698701456022 (text/html)
Access to full text is restricted to subscribers.

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:taf:jriskr:v:4:y:2001:i:1:p:49-62

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RJRR20

DOI: 10.1080/136698701456022

Access Statistics for this article

Journal of Risk Research is currently edited by Bryan MacGregor

More articles in Journal of Risk Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().