Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Local linear regression analysis for interval-valued data

  • Jang, Jungteak (Department of Statistics, Hankuk University of Foreign Studies) ;
  • Kang, Kee-Hoon (Department of Statistics, Hankuk University of Foreign Studies)
  • Received : 2020.01.24
  • Accepted : 2020.03.01
  • Published : 2020.05.31
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Abstract

Interval-valued data, a type of symbolic data, is given as an interval in which the observation object is not a single value. It can also occur frequently in the process of aggregating large databases into a form that is easy to manage. Various regression methods for interval-valued data have been proposed relatively recently. In this paper, we introduce a nonparametric regression model using the kernel function and a nonlinear regression model for the interval-valued data. We also propose applying the local linear regression model, one of the nonparametric methods, to the interval-valued data. Simulations based on several distributions of the center point and the range are conducted using each of the methods presented in this paper. Various conditions confirm that the performance of the proposed local linear estimator is better than the others.

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References

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