A nonparametric test for similarity of marginals—With applications to the assessment of population bioequivalence
- 作者：Gudrun Freitag ; Claudia Czado ; Axel Munk
- 关键词：Bioequivalence ; Cross-over trials ; Hadamard derivative ; Limit law ; Multivariate empirical process ; Pre– ; post comparison
- 刊名：Journal of Statistical Planning and Inference
- 出版年：2007
- 期刊代码：36_03783758
- 类别：mt
- 出版时间：1 March 2007
- 卷：137
- 期：3
- 页码：697-711
- 文件大小：354 K
摘要
In this paper we suggest a completely nonparametric test for the assessment of similar marginals of a multivariate distribution function. This test is based on the asymptotic normality of Mallows distance between marginals. It is also shown that the n out of n bootstrap is weakly consistent, thus providing a theoretical justification to the work in Czado, C. and Munk, A. [2001. Bootstrap methods for the nonparametric assessment of population bioequivalence and similarity of distributions. J. Statist. Comput. Simulation 68, 243–280]. The test is extended to cross-over trials and is applied to the problem of population bioequivalence, where two formulations of a drug are shown to be similar up to a tolerable limit. This approach was investigated in small samples using bootstrap techniques in Czado, C., Munk, A. [2001. Bootstrap methods for the nonparametric assessment of population bioequivalence and similarity of distributions. J. Statist. Comput. Simulation 68, 243–280], showing that the bias corrected and accelerated bootstrap yields a very accurate and powerful finite sample correction. A data example is discussed.