NONPARAMETRIC ESTIMATION WITH AGGREGATED DATA
Oliver Linton and
Yoon-Jae Whang
Econometric Theory, 2002, vol. 18, issue 2, 420-468
Abstract:
We introduce a kernel-based estimator of the density function and regression function for data that have been grouped into family totals. We allow for a common intrafamily component but require that observations from different families be independent. We establish consistency and asymptotic normality for our procedures. As usual, the rates of convergence can be very slow depending on the behavior of the characteristic function at infinity. We investigate the practical performance of our method in a simple Monte Carlo experiment.
Date: 2002
References: Add references at CitEc
Citations: View citations in EconPapers (11)
Downloads: (external link)
https://www.cambridge.org/core/product/identifier/ ... type/journal_article link to article abstract page (text/html)
Related works:
Working Paper: Nonparametric estimation with aggregated data (2002) 
Working Paper: Nonparametric Estimation with Aggregated Data (2000) 
Working Paper: Nonparametric estimation with aggregated data (2000) 
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:cup:etheor:v:18:y:2002:i:02:p:420-468_18
Access Statistics for this article
More articles in Econometric Theory from Cambridge University Press Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK.
Bibliographic data for series maintained by Kirk Stebbing ().