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A quantile regression models the relationship between the efficiency and knowledge indicators using a conditional quantile, evaluating the specific impact of these indicators on different groups of farms clustered on their level of efficiency.
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Quantile regression, on the other hand, aims to estimate a conditional quantile function for any quantile 0 < τ < 1.
We studied the effects of 37 BMI-associated SNPs in 75,230 adults of European ancestry across BMI percentiles by using conditional quantile regression (CQR) and meta-regression (MR) models.
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.
We used conditional quantile regression (QR) to assess the association between various percentiles of the VBP score distribution and hospital characteristics [ 17, 18].
Here we describe a new technique for estimating extremes using a quantile function model.
All arrays were normalized using a quantile normalization algorithm [44].
The estimated expression levels were normalized using a quantile normalization method [59].
Normalization of the arrays was performed using a "quantile" parameter.
Data were processed using a quantile normalization method.
Data was normalized using a quantile normalization procedure using R (http://www.rproject.org/).org/
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