Estimation of Dirichlet process priors with monotone missing data

Lei Yang and Xianyi Wu

Journal of Nonparametric Statistics, 2013, vol. 25, issue 4, 787-807

Abstract: This article investigates the estimation of Dirichlet process priors DP(α, α¯) of a random ( J +1)-dimensional distribution by monotone missing observations, where the precision parameter α is a positive scalar and α¯ a probability measure on ℝ-super- J +1. While α is estimated by maximising a particularly designed likelihood function, α¯ is estimated using kernel smoothing. The asymptotic properties show that the estimate of α is strongly consistent and asymptotically normally distributed. For the estimate of α¯, the L 1 consistency and the optimal bandwidths under an asymptotic mean integrated squared error criterion are examined. Finally, the performance of these estimates are analysed by means of a small simulation.

Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/10485252.2013.804074 (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:gnstxx:v:25:y:2013:i:4:p:787-807

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20

DOI: 10.1080/10485252.2013.804074

Access Statistics for this article

Journal of Nonparametric Statistics is currently edited by Jun Shao

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