## Demo of nonlinear quantile regression model based on Frank copulavFrank <- function(x, df, delta, u) # 某个非线性过程,得到的是[0,1]的值-log(1-(1-exp(-delta))/(1+exp(-delta*pt(x,df))*((1/u)-1)))/delta# 非线性模型FrankModel <- function(x, delta, mu,sigma, df, tau...
此基础上，1978年Koenker和Bassett[3]把中位数回归推⼴到了⼀般的分位数回归（Quantile Regression）上。分位数回归相对于最⼩⼆乘回归，应⽤条件更加宽松，挖掘的信息更加丰富。它依据因变量的 条件分位数对⾃变量X进⾏回归，这样得到了所有分位数下的回归模型。因此分位数回归相⽐普 通的最⼩...
For instance, quantile regression relies on minimizing the conditional quantile loss, which is based on the quantile check function (Koenker and Bassett Jr 1978). This has been extended to more flexible regression functions such as the quantile regression forest (Meinshausen 2006) and the gradient ...
Quantile regression models have become a widely used statistical tool in genetics and in the omics fields because they can provide a rich description of the predictors’ effects on an outcome without imposing stringent parametric assumptions on the outcome-predictors relationship. This work considers the...
where the function ρτ(⋅) is given by ρτ(ϵ)=τϵ, if ϵ⩾0, and ρτ(ϵ)=(τ−1)ϵ otherwise (see Koenker and Bassett [15]). This so-called ‘check function’ ρτ(⋅) replaces the traditional quadratic loss used for mean regression. In this frequentist semipar...
DeepQuantreg implements a deep neural network to the quantile regression for survival data with right censoring, which is adjusted by the inverse of the estimated censoring distribution in the check function. DeepQuantreg shows that the deep learning method could be flexible enough to predict nonlinear...
In this paper, we consider the empirical estimator of the cumulative quantile regression (CQR) functionwhen the covariate is subjected to random truncation and censorship. Strong Gaussian approximations for the associated CQR process are established under appropriate assumptions. A functional law of the ...
()method in bothKRRandANN, there is asmoothoption available. When set toTRUE, it uses the Gaussian kernel convoluted check loss. For fitting nonparametric ES regression using nonparametrically generated surrogate response variables, thees()function provides two options:squared loss(robust=FALSE) and ...
Martina Pons Minimum distance quantile regression November 18, 2022 1 / 17 Introduction Model and estimator Asymptotics Traditional Panel Grouped Stata commands Application Summary • We are interested in the effect of some treatment variables on the distribution of an outcome =⇒ quantile regression...
We introduce a group penalized pseudo quantile regression (GPQR) framework with both group-lasso and group non-convex penalties. We approximate the quantile regression check function using a pseudo-quantile check function. Then, using the majorization鈥搈inimization principle, we derive a simple and...