Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/238805 
Year of Publication: 
2013
Citation: 
[Journal:] International Econometric Review (IER) [ISSN:] 1308-8815 [Volume:] 5 [Issue:] 1 [Publisher:] Econometric Research Association (ERA) [Place:] Ankara [Year:] 2013 [Pages:] 20-42
Publisher: 
Econometric Research Association (ERA), Ankara
Abstract: 
Nonparametric density estimation is of great importance when econometricians want to model the probabilistic or stochastic structure of a data set. This comprehensive review summarizes the most important theoretical aspects of kernel density estimation and provides an extensive description of classical and modern data analytic methods to compute the smoothing parameter. Throughout the text, several references can be found to the most up-to-date and cut point research approaches in this area, while econometric data sets are analyzed as examples. Lastly, we present SIZer, a new approach introduced by Chaudhuri and Marron (2000), whose objective is to analyze the visible features representing important underlying structures for different bandwidths.
Subjects: 
Nonparametric Density Estimation
SiZer
Plug-In Bandwidth Selectors
Cross- Validation
Smoothing Parameter
JEL: 
C14
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.