Web19 feb. 2014 · Quantile regression plays an important role in many applications, including outlier-robust exploratory analysis in gene selection. In addition, the sparsity … WebThis is an experimental function to find the smoothing parameter for a quantile or robust spline using a more appropriate criterion than mean squared error prediction. The quantile spline is found by an iterative algorithm using weighted least squares cubic splines.

回归损失函数：L1，L2，Huber，Log-Cosh，Quantile …

WebF.Hampeletal./ComputationalStatisticsandDataAnalysis55(2011)324 337 325 butalsotoML-estimatorsandPitmanestimators(Pitman,1939 ... Web17 dec. 2024 · Quantile Loss 分位数损失. 通常的回归算法是拟合训练数据的期望或者中位数，而使用分位数损失函数可以通过给定不同的分位点，拟合训练数据的不同分位数。. 如 … firmin koffi

Sparse Quantile Huber Regression for Efficient and Robust …

Web7 okt. 2024 · Smoothing quantile regression for streaming data sets For model (1.1), Dj={Xj,Yj}is the j-th batch data set, where Yj=(Y1,j,…,Ynj,j)⊤and Xj=(X1,j,…,Xnj,j)⊤. We suppose that the (Xi,j,Yi,j)for all is and js are i.i.d. samples from (Y,X). We begin with a simple scenario of two batches of data D1and D2, where D2arrives after D1. WebThe quantile loss function in model (2.2) is strongly convex but nonsmooth. It is ffi to calculate the fftial of the objective function. One way to study nonsmooth model is to … Webet al(2024) proposed a new smooth generalization of the quantile loss function along with statistical advantages of this loss function when estimating general additive quantile models.Yi and Huang(2024) proposed approximating the quantile loss function with the Huber loss to solve elastic net quantile regres- euler\u0027s backward method