Testing parametric models in linear-directional regression
Abstract
This paper presents a goodness-of-fit test for parametric regression models with scalar response and directional predictor, that is, a vector on a sphere of arbitrary dimension. The testing procedure is based on the weighted squared distance between a smooth and a parametric regression estimator, where the smooth regression estimator is obtained by a projected local approach. Asymptotic behavior of the test statistic under the null hypothesis and local alternatives is provided, jointly with a consistent bootstrap algorithm for application in practice. A simulation study illustrates the performance of the test in finite samples. The procedure is applied to test a linear model in text mining.
- Publication:
-
arXiv e-prints
- Pub Date:
- September 2014
- DOI:
- 10.48550/arXiv.1409.0506
- arXiv:
- arXiv:1409.0506
- Bibcode:
- 2014arXiv1409.0506G
- Keywords:
-
- Statistics - Methodology;
- 62G10;
- 62H11;
- 62G08;
- 62G09
- E-Print:
- 13 pages, 3 figures. Supplementary material: 22 pages, 9 figures, 3 tables