scholarly journals Improved local quantile regression

2018 ◽  
Vol 19 (5) ◽  
pp. 501-523
Author(s):  
Xi Liu ◽  
Keming Yu ◽  
Qifa Xu ◽  
Xueqing Tang
Keyword(s):  
Quantile Regression ◽  
Real Data ◽  
Laplace Distribution ◽  
Regression Method ◽  
Simulation Studies ◽  
Weighted Likelihood ◽  
Scale Mixture ◽  
Good Design ◽  
Statistical Planning ◽  
Quantile Regression Method

We investigate a new kernel-weighted likelihood smoothing quantile regression method. The likelihood is based on a normal scale-mixture representation of asymmetric Laplace distribution (ALD). This approach enjoys the same good design adaptation as the local quantile regression ( Spokoiny et al., 2013 , Journal of Statistical Planning and Inference, 143, 1109–1129), particularly for smoothing extreme quantile curves, and ensures non-crossing quantile curves for any given sample. The performance of the proposed method is evaluated via extensive Monte Carlo simulation studies and one real data analysis.

scholarly journals Power Prior Elicitation in Bayesian Quantile Regression

2011 ◽  
Vol 2011 ◽  
pp. 1-16 ◽  
Author(s):  
Rahim Alhamzawi ◽  
Keming Yu
Keyword(s):  
Quantile Regression ◽  
Prior Distribution ◽  
Likelihood Function ◽  
Real Data ◽  
Laplace Distribution ◽  
Scale Mixture ◽  
Prior Elicitation ◽  
Bayesian Quantile Regression ◽  
Critical Issues ◽  
Power Prior

We address a quantile dependent prior for Bayesian quantile regression. We extend the idea of the power prior distribution in Bayesian quantile regression by employing the likelihood function that is based on a location-scale mixture representation of the asymmetric Laplace distribution. The propriety of the power prior is one of the critical issues in Bayesian analysis. Thus, we discuss the propriety of the power prior in Bayesian quantile regression. The methods are illustrated with both simulation and real data.

scholarly journals Modified Least Trimmed Quantile Regression to Overcome Effects of Leverage Points

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Habshah Midi ◽  
Taha Alshaybawee ◽  
Mohammed Alguraibawi
Keyword(s):  
Quantile Regression ◽  
Simulation Study ◽  
Mahalanobis Distance ◽  
Real Data ◽  
Regression Method ◽  
Least Trimmed Squares ◽  
Leverage Points ◽  
Cutoff Points ◽  
Quantile Regression Method

Quantile regression estimates are robust for outliers in y direction but are sensitive to leverage points. The least trimmed quantile regression (LTQReg) method is put forward to overcome the effect of leverage points. The LTQReg method trims higher residuals based on trimming percentage specified by the data. However, leverage points do not always produce high residuals, and hence, the trimming percentage should be specified based on the ratio of contamination, not determined by a researcher. In this paper, we propose a modified least trimmed quantile regression method based on reweighted least trimmed squares. Robust Mahalanobis’ distance and GM6 weights based on Gervini and Yohai’s (2003) cutoff points are employed to determine the trimming percentage and to detect leverage points. A simulation study and real data are considered to investigate the performance of our proposed methods.

scholarly journals Simulation Study The Using of Bayesian Quantile Regression in Nonnormal Error

CAUCHY ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 121
Author(s):  
Catrin Muharisa ◽  
Ferra Yanuar ◽  
Dodi Devianto
Keyword(s):  
Confidence Interval ◽  
Quantile Regression ◽  
Simulation Study ◽  
Likelihood Function ◽  
Laplace Distribution ◽  
Regression Method ◽  
Bayesian Regression ◽  
Asymmetric Laplace Distribution ◽  
Bayesian Quantile Regression ◽  
Quantile Regression Method

The purposes of this paper is  to introduce the ability of the Bayesian quantile regression method in overcoming the problem of the nonnormal errors using asymmetric laplace distribution on simulation study. <strong>Method: </strong>We generate data and set distribution of error is asymmetric laplace distribution error, which is non normal data.  In this research, we solve the nonnormal problem using quantile regression method and Bayesian quantile regression method and then we compare. The approach of the quantile regression is to separate or divide the data into any quantiles, estimate the conditional quantile function and minimize absolute error that is asymmetrical. Bayesian regression method used the asymmetric laplace distribution in likelihood function. Markov Chain Monte Carlo method using Gibbs sampling algorithm is applied then to estimate the parameter in Bayesian regression method. Convergency and confidence interval of parameter estimated are also checked. <strong>Result: </strong>Bayesian quantile regression method results has more significance parameter and smaller confidence interval than quantile regression method. <strong>Conclusion: </strong>This study proves that Bayesian quantile regression method can produce acceptable parameter estimate for nonnormal error.

scholarly journals Friction Characteristics of Post-Tensioned Tendons of Full-Scale Structures Based on Site Tests

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Haoyun Yuan ◽  
Yuan Li ◽  
Bin Zhou ◽  
Shuanhai He ◽  
Peizhi Wang
Keyword(s):  
Friction Coefficient ◽  
Quantile Regression ◽  
Least Square Method ◽  
Regression Method ◽  
Full Scale ◽  
Least Square ◽  
Friction Characteristics ◽  
Bayesian Quantile Regression ◽  
Prestressing Force ◽  
Quantile Regression Method

In the design of prestressing concrete structures, the friction characteristics between strands and channels have an important influence on the distribution of prestressing force, which can be considered comprehensively by curvature and swing friction coefficients. However, the proposed friction coefficient varies widely and may lead to an inaccurate prestress estimation. In this study, four full-scale field specimens were established to measure the friction loss of prestressing tendons with electromagnetic sensors and anchor cable dynamometers to evaluate the friction coefficient. The least square method and Bayesian quantile regression method were adopted to calculate the friction coefficient, and the results were compared with that in the specifications. Field test results showed that Bayesian quantile regression method was more effective and significant in the estimation of the friction coefficient.

scholarly journals Statistically Efficient Construction of α-Risk-Minimizing Portfolio

2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
Hiroyuki Taniai ◽  
Takayuki Shiohama
Keyword(s):  
Monte Carlo ◽  
Quantile Regression ◽  
Asymptotic Normality ◽  
Asymptotic Properties ◽  
Regression Method ◽  
General Reference ◽  
Regression Problem ◽  
Efficient Construction ◽  
Consistency And Asymptotic Normality ◽  
Quantile Regression Method

We propose a semiparametrically efficient estimator for α-risk-minimizing portfolio weights. Based on the work of Bassett et al. (2004), an α-risk-minimizing portfolio optimization is formulated as a linear quantile regression problem. The quantile regression method uses a pseudolikelihood based on an asymmetric Laplace reference density, and asymptotic properties such as consistency and asymptotic normality are obtained. We apply the results of Hallin et al. (2008) to the problem of constructing α-risk-minimizing portfolios using residual signs and ranks and a general reference density. Monte Carlo simulations assess the performance of the proposed method. Empirical applications are also investigated.

scholarly journals ANALISIS PERANAN EMAS DAN OBLIGASI PEMERINTAH SEBAGAI SAFE HAVEN PERIODE 2014—2018

2020 ◽  
Vol 16 (2) ◽  
pp. 212-236
Author(s):  
Evamelia Evamelia ◽  
Yunia Panjaitan
Keyword(s):  
Quantile Regression ◽  
Capital Market ◽  
Regression Method ◽  
Government Policies ◽  
Safe Haven ◽  
Government Bonds ◽  
Bear Market ◽  
Political Conditions ◽  
Quantile Regression Method

The purpose of this research to identify the role of gold and government bonds role as safe haven in Indonesian capital market during 2014-2018. In this study we analyze the influence of stock on gold and government return on bear market conditions, using quantile regression. The quantile regression method was used to analyze the data.  The result if this study indicated that gold and government bonds cannot play a safe haven consistently throughout the study period due to political conditions, government policies and psychological factors (doubt) from investors. For the following research, researchers should examine more deeply about the factors that influence the loss of the role of safe haven in both investment instruments.

scholarly journals EFEKTIVITAS REGRESI KUANTIL DALAM MENGATASI PENCILAN PONTENSIAL

2020 ◽  
Vol 14 (2) ◽  
pp. 305-312
Author(s):  
Netti Herawati
Keyword(s):  
Quantile Regression ◽  
Mean Square Error ◽  
Akaike Information Criterion ◽  
Robust Regression ◽  
Information Criterion ◽  
Regression Method ◽  
Least Square ◽  
Mean Square ◽  
The Impact ◽  
Quantile Regression Method

Abstrak Regresi kuantil sebagai metode regresi yang robust dapat digunakan untuk mengatasi dampak kasus yang tidak biasa pada estimasi regresi. Tujuan dari penelitian ini adalah untuk mengevaluasi efektivitas regresi kuantil untuk menangani pencilan potensial dalam regresi linear berganda dibandingkan dengan metode kuadrat terkecil (MKT). Penelitian ini menggunakan data simulasi dengan p=3; n = 20, 40, 60, 100, 200 and   and  diulang 1000 kali. Efektivitas metode regresi kuantil dan MKT dalam pendugaan parameter β diukur dengan Mean square error (MSE) dan Akaike Information Criterion (AIC). Hasil penelitian menunjukkan bahwa regresi kuantil mampu menangani pencilan potensial dan memberikan penaksir yang lebih baik dibandingkan dengan MKT berdasarkan nilai MSE dan AIC. Kata kunci: AIC, MSE, pencilan, regresi kuantil Abstract Quantitative regression as a robust regression method can be used to overcome the impact of unusual cases on regression estimation. The purpose of this study is to evaluate the effectiveness of quantile regression to deal with potential outliers in multiple linear regression compared to the least squares methodordinary least square (OLS).   This study uses simulation data with p=3; n = 20, 40, 60, 100, 200 and   and  repeated 1000 times. The effectiveness of the quantile regression method and OLS in estimating β   parameters was measured by Mean square error (MSE) and Akaike Information Criterion (AIC). The results showed that quantile regression was able to handle potential outliers and provide better predictors compared to MKT based on MSE and AIC values. Keywords: AIC, MSE, outliers, quantile regression

scholarly journals Bayesian Quantile Regression Method to Construct the Low Birth Weight Model

2019 ◽  
Vol 1245 ◽  
pp. 012044
Author(s):  
Ferra Yanuar ◽  
Aidinil Zetra ◽  
Catrin Muharisa ◽  
Dodi Devianto ◽  
Arrival Rince Putri ◽  
...  
Keyword(s):  
Birth Weight ◽  
Low Birth Weight ◽  
Quantile Regression ◽  
Regression Method ◽  
Bayesian Quantile Regression ◽  
Quantile Regression Method ◽  
Weight Model
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