Quantiles, Conditional Quantiles, Confidence Quantiles for p, Logodds(p)

Emanuel Parzen

Communications in Statistics - Theory and Methods, 2009, vol. 38, issue 16-17, 3048-3058

Abstract: This article discusses (1) our research to provide a framework for almost all of statistical methods for simple data, (2) need to plan the future of the “Science of Statistics” in order to compete for leadership in the practice of the “Statistics of Science”, (3) grand unifying ideas of the Science of Statistics, (4) an elegant rigorous proof when quantile function minimizes check loss function which is the basis of quantile regression, and (5) exact and approximate confidence quantiles (confidence interval endpoint functions) for parameters p and logodds(p) given a sample of a 0-1 variable.

Date: 2009
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610920902947535 (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:lstaxx:v:38:y:2009:i:16-17:p:3048-3058

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610920902947535

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().