Model selection in radon data fusion

Zhang Xuze (), Pyne Saumyadipta () and Kedem Benjamin ()
Additional contact information
Zhang Xuze: Department of Mathematics and Institute for Systems Research, University of Maryland, College Park. United States
Pyne Saumyadipta: Public Health Dynamics Laboratory, Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh. United States
Kedem Benjamin: Department of Mathematics and Institute for Systems Research, University of Maryland, College Park. United States

Statistics in Transition New Series, 2020, vol. 21, issue 4, 159-165

Abstract: Fitting parametric models or the use of the empirical cumulative distribution function are problematic when it comes to the estimation of tail probabilities from small samples. A possible remedy is to fuse or combine the small samples with additional data from external sources and base the inference on the so called density ratio model with variable tilt functions, which widens the support of the estimated distribution of interest. This approach is illustrated using residential radon concentration data collected from western Pennsylvania.

Keywords: Tail probabilities; density ratio model; variable tilt functions; Appalachian Plateau; Forest County; Pennsylvania. (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.21307/stattrans-2020-036 (text/html)

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:vrs:stintr:v:21:y:2020:i:4:p:159-165:n:1

DOI: 10.21307/stattrans-2020-036

Access Statistics for this article

Statistics in Transition New Series is currently edited by Włodzimierz Okrasa

More articles in Statistics in Transition New Series from Statistics Poland
Bibliographic data for series maintained by Peter Golla ().