Testing the Predictor Effect on a Functional Response

作者：Patilea Valentin^*; Sanchez Sellero Cesar; Saumard Matthieu

来源：Journal of the American Statistical Association, 2016, 111(516): 1684-1695.

DOI：10.1080/01621459.2015.1110031

• Functional data regression
• Goodness-of-fit test
• Karhunen-Loeve decomposition
• Nearest neighbor smoothing
• U-statistics

This article examines the problem of nonparametric testing,for the no-effect of a random covariate (or predictor) on a functional response. This means testing whether the conditional expectation of the response given the covariate is almost surely zero or not, without imposing any model relating response and covariate. The covariate could be univariate, multivariate, or functional. Our test statistic is a quadratic form involving univariate nearest neighbor smoothing and the asymptotic critical values are given by the standard normal law. When the covariate is multidimensional or functional, a preliminary dimension reduction device is used, which allows the effect of the covariate to be summarized into a univariate random quantity. The test is able to detect not only linear but nonparametric alternatives. The responses could have conditional variance of unknown form and the law of the covariate does not need to be known. An empirical study with simulated and real data shows that the test performs well, n applications. Supplementary materials for this article are available online.

• 出版日期2016-12