IMPROVING ESTIMATIONS IN QUANTILE REGRESSION MODEL WITH AUTOREGRESSIVE ERRORS
Küçük Resim Yok
Tarih
2018
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Vinca Inst Nuclear Sci
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
Anahtar Kelimeler
preliminary estimation, Stein-type estimation, autocorrelation, quantile regression
Kaynak
Thermal Science
WoS Q Değeri
Q3
Scopus Q Değeri
Q3
Cilt
22
