THERMAL SCIENCE

International Scientific Journal

Bahadır Yüzbaşi

,

Yasin Asar

,

M. Şamil Şik

,

Ahmet Demiralp

IMPROVING ESTIMATIONS IN QUANTILE REGRESSION MODEL WITH AUTOREGRESSIVE ERRORS

ABSTRACT
An important issue is that the respiratory mortality may be a result of air pollution which can be measured by the following variables: temperature, relative humidity, carbon monoxide, sulfur dioxide, nitrogen dioxide, hydrocarbons, ozone, and particulates. The usual way is to fit a model using the ordinary least squares regression, which has some assumptions, also known as Gauss-Markov assumptions, on the error term showing white noise process of the regression model. However, in many applications, especially for this example, these assumptions are not satisfied. Therefore, in this study, a quantile regression approach is used to model the respiratory mortality using the mentioned explanatory variables. Moreover, improved estimation techniques such as preliminary testing and shrinkage strategies are also obtained when the errors are autoregressive. A Monte Carlo simulation experiment, including the quantile penalty estimators such as lasso, ridge, and elastic net, is designed to evaluate the performances of the proposed techniques. Finally, the theoretical risks of the listed estimators are given.
KEYWORDS
preliminary estimation, stein-type estimation, autocorrelation, quantile regression
PAPER SUBMITTED: 2017-06-12
PAPER REVISED: 2017-11-15
PAPER ACCEPTED: 2017-11-24
PUBLISHED ONLINE: 2018-01-07
DOI REFERENCE: https://doi.org/10.2298/TSCI170612275Y
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2018, VOLUME 22, ISSUE Supplement 1, PAGES [S97 - S107]
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Improving estimations in quantile regression model with autoregressive errors

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence