A semiparametric location-scale regression model with semi-heavy tails based on hyperbolic secant distribution

Document Type : Original Paper

Authors

1 Department of Statistics, Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran

2 Department of Statistics, Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood , Iran

Abstract

Practitioners who use the classical regression model have been realized that many of its assumptions seldom hold. We then need flexible models to capture the real intrinsic properties of data. The class of generalized additive models for location, scale, and shape is very flexible in analyzing the inherent complexity of the data. This class of models provides the ability to do regression modeling beyond the mean of the response variable. Indeed, to admit outliers in the modeling framework is vital. Where we have a few outliers, the model could be too complicated by using heavy-tailed distributions. To overcome this issue, in this paper, we introduce a new location-scale semiparametric regression that is constructed based on a semi-heavy-tailed distribution, named hyperbolic secant, in the considered class of the models. We explore the performance of the proposed model by a simulation study and compare the results with a classical normal model. We also illustrate the model in a real application.

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Journal of Advanced Mathematical Modeling
Volume 10, Issue 2
December 2020
Pages 379-399

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History

  • Receive Date: 26 September 2019
  • Revise Date: 18 May 2020
  • Accept Date: 23 May 2020
  • First Publish Date: 21 December 2020

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