Reference curves based on non-parametric quantile regression

2002 ◽  
Vol 21 (20) ◽  
pp. 3119-3135 ◽  
Author(s):  
Ali Gannoun ◽  
St�phane Girard ◽  
Christiane Guinot ◽  
J�r�me Saracco
Keyword(s):  
Quantile Regression ◽  
Reference Curves ◽  
Non Parametric

scholarly journals Variable selection for non-parametric quantile regression via smoothing spline analysis of variance

Stat ◽  
2013 ◽  
Vol 2 (1) ◽  
pp. 255-268 ◽  
Author(s):  
Chen-Yen Lin ◽  
Howard Bondell ◽  
Hao Helen Zhang ◽  
Hui Zou
Keyword(s):  
Variable Selection ◽  
Quantile Regression ◽  
Analysis Of Variance ◽  
Smoothing Spline ◽  
Selection For ◽  
Non Parametric

scholarly journals Quantile Non‑parametric Additive Models

2019 ◽  
Vol 6 (345) ◽  
pp. 127-139
Author(s):  
Grażyna Trzpiot
Keyword(s):  
Quantile Regression ◽  
Stock Exchange ◽  
Risk Measure ◽  
Additive Models ◽  
Central Tendency ◽  
Data Set ◽  
Response Variable ◽  
Parametric Regression ◽  
Warsaw Stock Exchange ◽  
Non Parametric

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.

scholarly journals The impact of accommodation entrepreneurship activities on Islamic tourism (Umrah) development: An empirical evidence from Saudi Arabia

2020 ◽  
Vol 1 (2) ◽  
pp. 111-118
Author(s):  
Mohammed Ayoub Ledhem ◽  
Warda Moussaoui
Keyword(s):  
Saudi Arabia ◽  
Quantile Regression ◽  
Empirical Evidence ◽  
Tourism Industry ◽  
Parametric Approach ◽  
Independent Variable ◽  
Main Factors ◽  
The Impact ◽  
Non Parametric

Currently, academics have given an intense interest in investigating the factors that promote the tourism industry as evidenced by the growing studies which investigate the impact of accommodation services as one of the main factors in the tourism industry. However, this investigated impact is missing in the Islamic tourism industry. For this reason, this paper is filling this gap by investigating the impact of accommodation entrepreneurship activities on Islamic tourism (Umrah pilgrimage) in Saudi Arabia. This paper applied a robust non-parametric approach of bootstrapped quantile regression to estimate the effect of accommodation entrepreneurship activities on Islamic tourism (Umrah pilgrimage) using a sample of the total Umrah pilgrims (Islamic tourists) as a proxy for Islamic tourism (Umrah pilgrimage) development and total accommodation entrepreneurship activities as an independent variable covering a period from 2010 until 2018. The findings demonstrated that accommodation entrepreneurship activities are promoting Islamic tourism (Umrah pilgrimage) industry in Saudi Arabia. The findings also indicated that accommodation entrepreneurship activities are one of the main factors that promote Islamic tourism (Umrah) in Saudi Arabia alongside Islamic life and belief, and religious loyalty.

RISK MARGIN QUANTILE FUNCTION VIA PARAMETRIC AND NON-PARAMETRIC BAYESIAN APPROACHES

2015 ◽  
Vol 45 (3) ◽  
pp. 503-550 ◽  
Author(s):  
Alice X.D. Dong ◽  
Jennifer S.K. Chan ◽  
Gareth W. Peters
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Loss Function ◽  
Regression Models ◽  
Quantile Function ◽  
Parametric Model ◽  
Parametric Models ◽  
Risk Margin ◽  
Quantile Functions ◽  
Non Parametric

AbstractWe develop quantile functions from regression models in order to derive risk margin and to evaluate capital in non-life insurance applications. By utilizing the entire range of conditional quantile functions, especially higher quantile levels, we detail how quantile regression is capable of providing an accurate estimation of risk margin and an overview of implied capital based on the historical volatility of a general insurers loss portfolio. Two modeling frameworks are considered based around parametric and non-parametric regression models which we develop specifically in this insurance setting. In the parametric framework, quantile functions are derived using several distributions including the flexible generalized beta (GB2) distribution family, asymmetric Laplace (AL) distribution and power-Pareto (PP) distribution. In these parametric model based quantile regressions, we detail two basic formulations. The first involves embedding the quantile regression loss function from the nonparameteric setting into the argument of the kernel of a parametric data likelihood model, this is well known to naturally lead to the AL parametric model case. The second formulation we utilize in the parametric setting adopts an alternative quantile regression formulation in which we assume a structural expression for the regression trend and volatility functions which act to modify a base quantile function in order to produce the conditional data quantile function. This second approach allows a range of flexible parametric models to be considered with different tail behaviors. We demonstrate how to perform estimation of the resulting parametric models under a Bayesian regression framework. To achieve this, we design Markov chain Monte Carlo (MCMC) sampling strategies for the resulting Bayesian posterior quantile regression models. In the non-parametric framework, we construct quantile functions by minimizing an asymmetrically weighted loss function and estimate the parameters under the AL proxy distribution to resemble the minimization process. This quantile regression model is contrasted to the parametric AL mean regression model and both are expressed as a scale mixture of uniform distributions to facilitate efficient implementation. The models are extended to adopt dynamic mean, variance and skewness and applied to analyze two real loss reserve data sets to perform inference and discuss interesting features of quantile regression for risk margin calculations.

Bayesian non-parametric quantile regression using splines for modelling wave heights

2008 ◽  
pp. 1055-1061
Author(s):  
Paul Thompson ◽  
Dominic Reeve ◽  
Julian Stander ◽  
Yuzhi Cai ◽  
Rana Moyeed
Keyword(s):  
Quantile Regression ◽  
Wave Heights ◽  
Non Parametric
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