Sparse Data, Estimational Reliability, and Risk-Efficient Decisions

Jock Anderson ()

American Journal of Agricultural Economics, 1974, vol. 56, issue 3, 564-572

Abstract: The minimal data required for a reasonable estimation of probability distributions is investigated through a Monte Carlo study of a rule for smoothing sparse data into cumulative distribution functions. In a set of estimated distributions, risky prospects not preferred by risk-averse decision makers can be identified and discarded.

Date: 1974
References: Add references at CitEc
Citations: View citations in EconPapers (17)

Downloads: (external link)
http://hdl.handle.net/10.2307/1238609 (application/pdf)
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:oup:ajagec:v:56:y:1974:i:3:p:564-572.

Access Statistics for this article

American Journal of Agricultural Economics is currently edited by Madhu Khanna, Brian E. Roe, James Vercammen and JunJie Wu

More articles in American Journal of Agricultural Economics from Agricultural and Applied Economics Association Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press ().