A Review of Kernel Density Estimation with Applications to Econometrics

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: International Econometric Review (IER) ; ISSN: 1308-8815 ; Volume: 5 ; Year: 2013 ; Issue: 1 ; Pages: 20-42 ; Ankara: Econometric Research Association (ERA)

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General
• Subject: Nonparametric Density Estimation ; SiZer ; Plug-In Bandwidth Selectors ; Cross- Validation ; Smoothing Parameter

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Zambom, Adriano Z. ; Dias, Ronaldo
• Event: Veröffentlichung
• (who): Econometric Research Association (ERA)
• (where): Ankara
• (when): 2013

Further information

• Handle: http://hdl.handle.net/10419/238805
• Last update: 10.03.2025, 11:42 AM CET