quantile()R语言中的函数用于在概率[0, 1]的数据集中创建样本分位数。例如第一个分位数为 0.25[25%],第二个为 0.50[50%],第三个为 0.75[75%]。 用法:quantile(x) 参数: x:数据集 范例1: # R program to create# quantiles of a data set# Create a data framed<- data.frame( name = c("Ab...
Figure 4 shows the same QQplot as Figure 1, but this time in the typical ggplot2 design. Video, Further Resources & Summary Do you need more information on the R programming syntax of the present tutorial? Then you may want to have a look at the following video of my YouTube channel....
Quantile Regresion with Group Penalty using linear programming algorithmBen Sherwood
Previously, we described theessentials of R programmingand provided quick start guides forimporting dataintoR. Here, we’ll describe how to createquantile-quantileplots in R.QQ plot(or quantile-quantile plot) draws the correlation between a given sample and the normal distribution. A 45-degree ref...
Lepp, “Discrete approximation in quantile problem of portfolio selection,” in: S. Uryasev and P.M. Pardalos (eds.), Stochastic Optimization: Algorithms and Applications, Kluwer, Dordrecht (2001), pp. 119–133.A.I. Kibzun and R. Lepp, "Discrete approximation in quantile problem of ...
We can perform quantile regression in R easily with thequantregpackage. I will demonstrate how to use it on themtcarsdataset. (For more details on thequantregpackage, you can read the package’s vignettehere.) Let’s load our packages and data: ...
The remaining quantiles are calculated in the formBj+Bj+1−Bjr, wherej=q,r=fracq, andqis one of the quantities given below. 3. q=np; 4. q=np+12; 5. q=n+1p; 6. q=1+n−1p; 7. q=13+n+13p; (default method) ...
This package contains two modules: thelinearmodule, which implements convolution smoothed quantile regression in both low and high dimensions, and thejointmodule, designed for joint quantile and expected shortfall regression. For R implementation, see theconquerpackage onCRAN(also embedded inquantregas an...
Finally, we show numerical results of our algorithms in the context of an application involving risk-averse bidding for energy storage.doi:10.48550/arXiv.1509.01920Jiang, Daniel R.Powell, Warren B.MathematicsD. R. Jiang and W. B. Powell. Risk-averse approximate dynamic programming with quantile-...
In this paper the concept of quantile-based optimal portfolio selection is introduced and a specific portfolio connected to it, the conditional value-of-return (CVoR) portfolio, is proposed. The CVoR is defined as the mean excess return or the conditional value-at-risk (CVaR) of the return ...