The Smooth Colonel Meets the Reverend
Nicholas Kiefer () and
Working Papers from Cornell University, Center for Analytic Economics
Kernel smoothing techniques have attracted much attention and some notoriety in recent years. The attention is well deserved as kernel methods free researchers from having to impose rigid parametric structure on their data. The notoriety arises from the fact that the amount of smoothing (i.e., local averaging) that is appropriate for the problem at hand is under the control of the researcher. In this paper we provide a deeper understanding of kernel smoothing methods for discrete data by leveraging the unexplored links between hierarchical Bayesmodels and kernelmethods for discrete processes. A number of potentially useful results are thereby obtained, including bounds on when kernel smoothing can be expected to dominate non-smooth (e.g., parametric) approaches in mean squared error and suggestions for thinking about the appropriate amount of smoothing.
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4) Track citations by RSS feed
Downloads: (external link)
Journal Article: The smooth Colonel meets the Reverend (2009)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ecl:corcae:08-01
Access Statistics for this paper
More papers in Working Papers from Cornell University, Center for Analytic Economics Contact information at EDIRC.
Bibliographic data for series maintained by ().