A Parametric Quantile Regression Model for Asymmetric Response Variables on the Real Line
نویسندگان
چکیده
منابع مشابه
Flexible parametric quantile regression model
This article introduces regression quantile models using both RS and FKML generalised lambda distributions (GLD) and demonstrates the versatility of proposed models for a range of linear/non linear and heteroscedastic/homoscedastic empirical data. Owing to the rich shapes of GLDs, GLD quantile regression is a competitive flexible model compared to standard quantile regression. The proposed meth...
متن کاملSemi-parametric Quantile Regression for Analysing Continuous Longitudinal Responses
Recently, quantile regression (QR) models are often applied for longitudinal data analysis. When the distribution of responses seems to be skew and asymmetric due to outliers and heavy-tails, QR models may work suitably. In this paper, a semi-parametric quantile regression model is developed for analysing continuous longitudinal responses. The error term's distribution is assumed to be Asymmetr...
متن کاملParametric Quantile Regression Based on the Generalised Gamma Distribution
We explore a particular fully parametric approach to quantile regression and show that this approach can be very successful. Motivated by the provision of reference charts, we work in the specific context of a positive response variable, whose conditional distribution is modelled by the generalised gamma distribution, and a single covariate, the dependence of parameters of the generalised gamma...
متن کاملQuantile regression with multiple independent variables
Introduction Conclusions References Tables Figures Back Close Full Screen / Esc Abstract Introduction Conclusions References Tables Figures Back Close Full Screen / Esc Abstract This study further develops the method of quantile regression (QR) to predict exceedance probabilities of flood stages by post-processing forecasts. Using data from the 82 river gages, for which the National Weather Ser...
متن کاملLocal Polynomial Quantile Regression With Parametric Features
We propose a new approach to conditional quantile function estimation that combines both parametric and nonparametric techniques. At each design point, a global, possibly incorrect, pilot parametric model is locally adjusted through a kernel smoothing fit. The resulting quantile regression estimator behaves like a parametric estimator when the latter is correct and converges to the nonparametri...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Symmetry
سال: 2020
ISSN: 2073-8994
DOI: 10.3390/sym12121938