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arXiv · 1409.0506

Testing parametric models in linear-directional regression

Eduardo García-Portugués·Ingrid Van Keilegom·Rosa M. Crujeiras·Wenceslao González-Manteiga
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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.

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Eduardo García-Portugués, Ingrid Van Keilegom, Rosa M. Crujeiras, Wenceslao González-Manteiga. 2014-09-01. Testing parametric models in linear-directional regression. https://doi.org/10.1111/sjos.12236

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