Confidence intervals for nonparametric quantile regression: an emphasis on smoothing splines approach

2017 ◽  
Vol 59 (4) ◽  
pp. 527-543
Author(s):  
Yaeji Lim ◽  
Hee-Seok Oh
Keyword(s):  
Quantile Regression ◽  
Confidence Intervals ◽  
Smoothing Splines

A convergent algorithm for quantile regression with smoothing splines

1995 ◽  
Vol 19 (6) ◽  
pp. 613-630 ◽  
Author(s):  
Ronald J. Bosch ◽  
Yinyu Ye ◽  
George G. Woodworth
Keyword(s):  
Quantile Regression ◽  
Smoothing Splines ◽  
Convergent Algorithm

Computing confidence intervals from massive data via penalized quantile smoothing splines

2020 ◽  
Vol 144 ◽  
pp. 106885
Author(s):  
Likun Zhang ◽  
Enrique del Castillo ◽  
Andrew J. Berglund ◽  
Martin P. Tingley ◽  
Nirmal Govind
Keyword(s):  
Confidence Intervals ◽  
Smoothing Splines ◽  
Massive Data

scholarly journals Analysis of the adaptability of black bean cultivars by means of quantile regression

2019 ◽  
Vol 49 (3) ◽  
Author(s):  
Laís Mayara Azevedo Barroso ◽  
Moysés Nascimento ◽  
Leiri Daiane Barili ◽  
Ana Carolina Campana Nascimento ◽  
Naine Martins do Vale ◽  
...  
Keyword(s):  
Quantile Regression ◽  
Confidence Intervals ◽  
Genetic Improvement ◽  
Research Centers ◽  
Black Bean ◽  
Average Productivity ◽  
Research Institutes ◽  
Improvement Programs ◽  
Productivity Variable

ABSTRACT: The aim of this study was to use quantile regression (QR) to characterize the effect of the adaptability parameter throughout the distribution of the productivity variable on black bean cultivars launched by different national research institutes (research centers) over the last 50 years. For this purpose, 40 cultivars developed by Brazilian genetic improvement programs between 1959 and 2013 were used. Initially, QR models were adjusted considering three quantiles (τ = 0.2, 0.5 and 0.8). Subsequently, with the confidence intervals, quantile models τ = 0.2 and 0.8 (QR0.2 and QR0.8) showed differences regarding the parameter of adaptability and average productivity. Finally, by grouping the cultivars into one of the two groups defined from QR0.2 and QR0.8, it was reported that the younger cultivars were associated to the quantile τ = 0.8, i.e., those with higher yields and more responsive conditions indicating that genetic improvement over the last 50 years resulted in an increase in both the productivity and the adaptability of cultivars.

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