عنوان مقاله [English]
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.