decomposition analysis with quantile regressionQuantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one
基于分位数回归的分布强化学习(Distributional Reinforcemet Learning with Quantile Regression) 范围很大时,能够进行更精确的预测 避免了C51中的投影操作 这种再参数化允许我们使用分位数回归来最小化Wasserstein损失,而不受有偏梯度的影响。 3.QuantileRegression(分位数回归) 接下来就是重头戏——分位数回归,它是分布...
紧接着前面Distributional RL做的工作 原文传送门 Dabney, Will, et al. "Distributional reinforcement learning with quantile regression." Thirty-Second AAAI Conference on Artificial Intelligence…
分位数回归(quantileregression)R实现当研究收入分配等问题时我们可能主要关注的是处于分布低尾的穷人和分布高尾的富人等处于因变量非中心位置的情况而1条件均值模型主要考虑的是因变量的均值难以扩展到这种非中心位置此时只能使用分位数模型进行估计 分位数回归(quantileregression)R实现 信心很重要,不管是擅长或者不擅长...
## 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...
Gould, W. 1992. sg11.1: Quantile regression with bootstrapped standard errors.Stata Technical Bulletin 9: 19–21. Reprinted inStata Technical Bulletin Reprints, vol. 2, pp. 137–150. Gould, W., and W. H. Rogers. 1994. Quantile regression as an alternative to robust regression.Proceedings of...
Quantile regression models provide a wide picture of the conditional distributions of the response variable by capturing the effect of the covariates at different quantile levels. In most applications, the parametric form of those conditional distributions is unknown and varies across the covariate space...
## Demo of nonlinear quantile regression model based on Frank copula vFrank <- 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...
Quantile regression models the conditional quantiles of an outcome as a linear combination of predictors. Traditional quantile regression relies on a specific set of loss functions and linear programming for estimation. To introduce Bayesian quantile regression, Yu and Moyeed (2001) use an equivalent ...
uantile Regression. Quantile Regression.Quantile Regression.This article focuses on U.S. literature and researches on quantile regression analysis which seeks to extend ideas to the estimation of conditional quantile function. Quantiles seem inseparably linked to the operations of ordering and sorting ...