Name
Abstract | ||
|---|---|---|
The objective of this article is to present a depth based multivariate control quantile test using statistically equivalent blocks (DSEBS). Given a random sample {x"1,...,x"m} of R^d-valued random vectors (d=1) with a distribution function (DF) F, statistically equivalent blocks (SEBS), a multivariate generalization of the univariate sample spacings, can be constructed using a sequence of cutting functions h"i(x) to order x"i,i=1,...,m. DSEBS are data driven, center-outward layers of shells whose shapes reflect the underlying geometric features of the unknown distribution and provide a framework for selection and comparison of cutting functions. We propose a control quantile test, using DSEBS, to test the equality of two DFs in R^d. The proposed test is distribution free under the null hypothesis and well defined when d=max(m,n). A simulation study compares the proposed statistic to depth-based Wilcoxon rank sum test. We show that the new test is powerful in detecting the differences in location, scale and shape (skewness or kurtosis) changes in two multivariate distributions. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.csda.2012.06.013 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
data depth,distribution function,control quantile test,d-valued random vector,multivariate control quantile test,unknown distribution,multivariate generalization,multivariate distribution,new test,equivalent block,proposed test,nonparametric analysis | Econometrics,Skewness,Statistic,Multivariate statistics,Nonparametric statistics,Wilcoxon signed-rank test,Quantile,Statistics,Univariate,Kurtosis,Mathematics | Journal |
Volume | Issue | ISSN |
57 | 1 | 0167-9473 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenyu Liu | 1 | 0 | 0.34 |
| Reza Modarres | 2 | 40 | 9.30 |
| Mengta Yang | 3 | 0 | 0.68 |