EconPapers    
Economics at your fingertips  
 

Nonparametric k-sample test based on kernel density estimator for paired design

Pablo Martínez-Camblor

Computational Statistics & Data Analysis, 2010, vol. 54, issue 8, 2035-2045

Abstract: Comparing whether the marginal distribution functions of a k-dimensional random variable are equal or not is a classical problem in statistical inference. Usually, the parametric ANOVA repeat measures analysis or the nonparametric Friedman test are used. Both procedures allow us to detect differences among the location parameters but not among shapes or spreads of the involved distributions. The statistic which is based on the measure of the common area under the respective kernel density estimators is used in order to compare the equality among the marginal densities of a k-dimensional random variable. The BM algorithm is employed to select, automatically, the final bandwidth parameter. Its statistical power is studied from Monte Carlo simulations and a real data analysis is also considered.

Keywords: ANOVA; repeat; measures; Friedman; test; Paired; samples; Common; area; statistic (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-9473(10)00111-8
Full text for ScienceDirect subscribers only.

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:eee:csdana:v:54:y:2010:i:8:p:2035-2045

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2025-03-19
Handle: RePEc:eee:csdana:v:54:y:2010:i:8:p:2035-2045