Goodness of Fit in Quantile Regression Models

2020 ◽  
pp. 45-48
Author(s):  
Jorge M. Uribe ◽  
Montserrat Guillen
Keyword(s):  
Quantile Regression ◽  
Regression Models ◽  
Goodness Of Fit

scholarly journals Nonlinear quantile regression to describe the dry matter accumulation of garlic plants

2020 ◽  
Vol 50 (1) ◽  
Author(s):  
Guilherme Alves Puiatti ◽  
Paulo Roberto Cecon ◽  
Moysés Nascimento ◽  
Ana Carolina Campana Nascimento ◽  
Antônio Policarpo Souza Carneiro ◽  
...  
Keyword(s):  
Quantile Regression ◽  
Least Squares ◽  
Nonlinear Regression ◽  
Regression Models ◽  
Dry Matter ◽  
Goodness Of Fit ◽  
Least Squares Method ◽  
Ordinary Least Squares ◽  
Dry Matter Accumulation ◽  
Over Time

ABSTRACT: The objective of this study was to adjust nonlinear quantile regression models for the study of dry matter accumulation in garlic plants over time, and to compare them to models fitted by the ordinary least squares method. The total dry matter of nine garlic accessions belonging to the Vegetable Germplasm Bank of Universidade Federal de Viçosa (BGH/UFV) was measured in four stages (60, 90, 120 and 150 days after planting), and those values were used for the nonlinear regression models fitting. For each accession, there was an adjustment of one model of quantile regression (τ=0.5) and one based on the least squares method. The nonlinear regression model fitted was the Logistic. The Akaike Information Criterion was used to evaluate the goodness of fit of the models. Accessions were grouped using the UPGMA algorithm, with the estimates of the parameters with biological interpretation as variables. The nonlinear quantile regression is efficient for the adjustment of models for dry matter accumulation in garlic plants over time. The estimated parameters are more uniform and robust in the presence of asymmetry in the distribution of the data, heterogeneous variances, and outliers.

scholarly journals Forecasting Large Price Declines of the Nikkei Using the S&P 500 Implied Volatility

2016 ◽  
Vol 8 (1) ◽  
pp. 58
Author(s):  
Chikashi Tsuji
Keyword(s):  
Quantile Regression ◽  
Regression Models ◽  
Implied Volatility ◽  
Predictive Power ◽  
Downside Risk ◽  
Price Changes ◽  
Empirical Results ◽  
Left Tail

This paper empirically examines the forecast power of the previous day’s US implied volatility for large declines of the Nikkei by using several versions of quantile regression models. All our empirical results suggest that the previous day’s US S&P 500 implied volatility has forecast power for large price drops of the Nikkei 225 in Japan. Since we repeatedly and carefully tested the several left tail risks in price changes of the Nikkei and we also tested by using some different versions of quantile regression models, our evidence of the predictive power of the S&P 500 implied volatility for downside risk of the Nikkei is very robust.

scholarly journals Investigation into Interactions between Accident Consequences and Traffic Signs: A Bayesian Bivariate Tobit Quantile Regression Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xuecai Xu ◽  
Željko Šarić
Keyword(s):  
Quantile Regression ◽  
Regression Models ◽  
Close Relation ◽  
Warning Signs ◽  
Material Damage ◽  
Accident Rate ◽  
Traffic Signs ◽  
Accident Severity ◽  
Severity Levels ◽  
The Republic

This study intended to investigate the interactions between accident severity levels and traffic signs in state roads located in Croatia and explore the correlation between accident severity levels and heterogeneity attributed to unobserved factors. The data from 460 state roads between 2012 and 2016 were collected from Traffic Accident Database System maintained by the Republic of Croatia Ministry of the Interior. To address the correlation and heterogeneity, Bayesian bivariate Tobit quantile regression models were proposed, in which the bivariate framework addressed the correlation of residuals with Bayesian approach, while the Tobit quantile regression model accommodated the heterogeneity due to unobserved factors. By comparing the Bayesian bivariate Tobit quantile and mean regression models, the proposed quantile models showed priority to mean model. Results revealed that (1) low visibility and the number of invalid traffic signs per km increased the accident rate of material damage, death, or injury; (2) average speed limit exhibited a close relation with accident rate; and (3) the number of mandatory signs was more likely to reduce the accident rate of material damage, while the number of warning signs was significant for accident rate of death or injury.

Conditional empirical likelihood for quantile regression models

Metrika ◽  
2016 ◽  
Vol 80 (1) ◽  
pp. 1-16
Author(s):  
Wu Wang ◽  
Zhongyi Zhu
Keyword(s):  
Quantile Regression ◽  
Empirical Likelihood ◽  
Regression Models

Analysing the gold-stock nexus using VARMA-BEKK-AGARCH and Quantile regression models: New evidence from South Africa and Nigeria

2019 ◽  
Vol 61 ◽  
pp. 348-362 ◽  
Author(s):  
Adeolu O. Adewuyi ◽  
Olabanji B. Awodumi ◽  
Temitope T. Abodunde
Keyword(s):  
South Africa ◽  
Quantile Regression ◽  
Regression Models ◽  
New Evidence

scholarly journals The comparison of censored quantile regression methods in prognosis factors of breast cancer survival

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Akram Yazdani ◽  
Mehdi Yaseri ◽  
Shahpar Haghighat ◽  
Ahmad Kaviani ◽  
Hojjat Zeraati
Keyword(s):  
Breast Cancer ◽  
Quantile Regression ◽  
Survival Time ◽  
Censored Data ◽  
Regression Models ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Right Censored Data ◽  
Censored Quantile Regression ◽  
Covariate Effects

AbstractThe Cox proportional hazards model is a widely used statistical method for the censored data that model the hazard rate rather than survival time. To overcome complexity of interpreting hazard ratio, quantile regression was introduced for censored data with more straightforward interpretation. Different methods for analyzing censored data using quantile regression model, have been introduced. The quantile regression approach models the quantile function of failure time and investigates the covariate effects in different quantiles. In this model, the covariate effects can be changed for patients with different risk and is a flexible model for controlling the heterogeneity of covariate effects. We illustrated and compared five methods in quantile regression for right censored data included Portnoy, Wang and Wang, Bottai and Zhang, Yang and De Backer methods. The comparison was made through the use of these methods in modeling the survival time of breast cancer. According to the results of quantile regression models, tumor grade and stage of the disease were identified as significant factors affecting 20th percentile of survival time. In Bottai and Zhang method, 20th percentile of survival time for a case with higher unit of stage decreased about 14 months and 20th percentile of survival time for a case with higher grade decreased about 13 months. The quantile regression models acted the same to determine prognostic factors of breast cancer survival in most of the time. The estimated coefficients of five methods were close to each other for quantiles lower than 0.1 and they were different from quantiles upper than 0.1.

scholarly journals Adaptively weighted group Lasso for semiparametric quantile regression models

Bernoulli ◽  
2019 ◽  
Vol 25 (4B) ◽  
pp. 3311-3338
Author(s):  
Toshio Honda ◽  
Ching-Kang Ing ◽  
Wei-Ying Wu
Keyword(s):  
Quantile Regression ◽  
Regression Models ◽  
Group Lasso

Survival Analysis With Quantile Regression Models

2008 ◽  
Vol 103 (482) ◽  
pp. 637-649 ◽  
Author(s):  
Limin Peng ◽  
Yijian Huang
Keyword(s):  
Survival Analysis ◽  
Quantile Regression ◽  
Regression Models
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