Quantile Non‑parametric Additive Models

Grażyna Trzpiot

doi:10.18778/0208-6018.345.07

Authors

Grażyna Trzpiot University of Economics in Katowice, Faculty of Informatics and Communication Department of Demography and Economic Statistics https://orcid.org/0000-0002-5129-5764

DOI:

https://doi.org/10.18778/0208-6018.345.07

Keywords:

Quantile regression, nonparametric regression, additive model

Abstract

Quantile regression allows us to assess different possible impacts of covariates on different quantiles of a response variable. Additive models for quantile functions provide an attractive framework for non‑parametric regression applications focused on functions of the response instead of its central tendency. Total variation smoothing penalties can be used to control the smoothness of additive components. We write down a general approach to estimation and inference for additive models of this type. Quantile regression as a risk measure has been applied in sector portfolio analysis for a data set from the Warsaw Stock Exchange.

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