Testing parametric models inlinear-directional regression Articles uri icon

publication date

  • August 2016

start page

  • 1178

end page

  • 1191

issue

  • 4

volume

  • 43

International Standard Serial Number (ISSN)

  • 0303-6898

Electronic International Standard Serial Number (EISSN)

  • 1467-9469

abstract

  • This paper presents a goodness-of-fit test for parametric regression models withscalar response and directional predictor, that is, a vector on a sphere of arbitrary dimension. Thetesting procedure is based on the weighted squared distance between a smooth and a paramet-ric regression estimator, where the smooth regression estimator is obtained by a projected localapproach. Asymptotic behaviour of the test statistic under the null hypothesis and local alternativesis provided, jointly with a consistent bootstrap algorithm for application in practice. A simulationstudy illustrates the performance of the test in finite samples. The procedure is applied to test alinear model in text mining.scalar response and directional predictor, that is, a vector on a sphere of arbitrary dimension. Thetesting 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 localapproach. Asymptotic behaviour of the test statistic under the null hypothesis and local alternativesis provided, jointly with a consistent bootstrap algorithm for application in practice. A simulationstudy illustrates the performance of the test infite samples. The procedure is applied to test alinear model in text mining.

subjects

  • Mathematics
  • Statistics

keywords

  • bootstrap calibration; directional data; goodness-of-fit test; local linear regression