Identification of Expected Outcomes in a Data Error Mixing Model With Multiplicative Mean Independence

Brent Kreider () and John Pepper

ISU General Staff Papers from Iowa State University, Department of Economics

Abstract: We consider the problem of identifying a mean outcome in corrupt sampling where the observed outcome is drawn from a mixture of the distribution of interest and another distribution. Relaxing the contaminated sampling assumption that the outcome is statistically independent of the mixing process, we assess the identifying power of an assumption that the conditional means of the distributions differ by a factor of proportionality. For binary outcomes, we consider the special case that all draws from the alternative distribution are erroneous. We illustrate how these models can inform researchers about illicit drug use in the presence of reporting errors.

Date: 2011-01-01
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
https://dr.lib.iastate.edu/server/api/core/bitstre ... 8ecffbb1bfaa/content
Our link check indicates that this URL is bad, the error code is: 403 Forbidden

Related works:
Journal Article: Identification of Expected Outcomes in a Data Error Mixing Model With Multiplicative Mean Independence (2011)
Journal Article: Identification of Expected Outcomes in a Data Error Mixing Model With Multiplicative Mean Independence (2011)
Working Paper: Identification of Expected Outcomes in a Data Error Mixing Model with Multiplicative Mean Independence (2011)
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:isu:genstf:201101010800001256

Access Statistics for this paper

More papers in ISU General Staff Papers from Iowa State University, Department of Economics Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070. Contact information at EDIRC.
Bibliographic data for series maintained by Curtis Balmer ().