Bootstrap in the limit;
Cramer test;
multivariate two-sample tests;
rigid motion invariance;
NONPARAMETRIC COMPETITORS;
EFFICIENCY;
INTEGRALS;
D O I:
暂无
中图分类号:
O21 [概率论与数理统计];
C8 [统计学];
学科分类号:
020208 ;
070103 ;
0714 ;
摘要:
New rigid motion invariant tests for the multivariate two-sample problem are proposed. The test statistic is based on the inter-point distances between the two samples and the inter-point distances within each sample. The asymptotic null distribution of the test statistic is a weighted sum of squares of independent unit normal random variables, the weights being the eigenvalues of a certain Hilbert-Schmidt-operator depending on the unknown underlying distribution. An estimate of the limit distribution is obtained by replacing the unknown weights by the eigenvalues of a bootstrapped version of the operator. Quantiles of the estimate are chosen as critical values. The tests are shown to be consistent. Approximate Bahadur efficiencies computed for normal location alternatives, normal scale alternatives, and Lehmann's contaminated alternative are seen to coincide locally with Pitman efficiencies. The results are supported by a simulation study.
机构:
Beijing Normal Univ, Sch Stat, Beijing, Peoples R ChinaBeijing Normal Univ, Sch Stat, Beijing, Peoples R China
Jiang, Qing
Meintanis, Simos G.
论文数: 0引用数: 0
h-index: 0
机构:
Univ Athens, Dept Econ, Athens, Greece
North West Univ, Unit Business Math & Informat, Potchefstroom, South AfricaBeijing Normal Univ, Sch Stat, Beijing, Peoples R China
Meintanis, Simos G.
Zhu, Lixing
论文数: 0引用数: 0
h-index: 0
机构:
Beijing Normal Univ, Sch Stat, Beijing, Peoples R China
Hong Kong Baptist Univ, Dept Math, Hong Kong, Hong Kong, Peoples R ChinaBeijing Normal Univ, Sch Stat, Beijing, Peoples R China
Zhu, Lixing
FUNCTIONAL STATISTICS AND RELATED FIELDS,
2017,
: 145
-
154
机构:
Univ Penn, Wharton Sch, Dept Stat & Data Sci, 265 37th St, Philadelphia, PA 19104 USAUniv Penn, Wharton Sch, Dept Stat & Data Sci, 265 37th St, Philadelphia, PA 19104 USA
Chatterjee, A.
Bhattacharya, B. B.
论文数: 0引用数: 0
h-index: 0
机构:
Univ Penn, Wharton Sch, Dept Stat & Data Sci, 265 37th St, Philadelphia, PA 19104 USAUniv Penn, Wharton Sch, Dept Stat & Data Sci, 265 37th St, Philadelphia, PA 19104 USA