Sentence examples for uses a quantile regression from inspiring English sources
The applied focus of the paper adopts a Bayesian strategy and uses a quantile regression approach that has, as one aspect, the benefit of robustness compared to conditional mean regression, which is demonstrated using simulated data.
Toward this end, we used a quantile regression model to estimate risk.
Using a quantile regression approach, we observe that innovation is of crucial importance for a handful of 'superstar' fast-growth firms.
Here the value of using a quantile regression approach is compared with a classical regression analysis approach to study the relationships between educational outcomes and likely predictor variables.
Overall, these results confirm that gender gaps vary across the ability distribution, as observed in previous studies exploring gender differences using a quantile regression approach (Penner and Paret 2008; Robinson and Lubiensky 2011; Giambona and Porcu 2015; Contini et al. 2016) and in Baye and Monseur (2016) meta-analysis on databases from IEA and PISA international surveys.
We, therefore, use a quantile regression (QR) approach to assess how child nutrition outcomes are related to individual and household characteristics.
We estimated outdoor recreation potential level using a quantile multiple regression, analysis of the 0.9 quantile of campsite density with nine landscape metrics determinants of ecosystem, service supply.
For both males and females, a quantile regression model using a 4th-order polynomial in age was ultimately fitted to NCs.
Additionally, a subanalysis in the group with retinopathy was performed to assess the association of the level of retinopathy, the severity of macular edema, and previous photocoagulation treatment with the ADDQoL and DTSQ scores using a Kruskal-Wallis test and a quantile regression model for median trend for the first two variables and the Mann–Whitney test for the photocoagulation treatment.
A quantile regression approach was used for data analysis.
Using this empirical distribution, we then use a log-quantile regression of the top 10% of these to their theoretical expected values (conditioned on an estimate of π, which is estimated as the proportion of empirical null test statistics greater than zero).
