Lasso Penalized Quantile RegressionR. Koenker
这里代码的意思是,先调用penalized包,之后,利用包中的函数penalized包实现LASSO变量筛选,模型中的Surv(OS, Death)这里是因变量生存时间、生存状态;penalized=hepatoCellularNoMissing[,23:48] 指模型LASSO筛选的变量是数据中的第23至48列的26个基因位点;standardize=T 是指对数据做标化处理(LASSO的要求,矩阵非奇异);...
Regularization Paths for Huber Loss Regression and Quantile Regression Penalized by Lasso or Elastic-Net To install: the released version from CRAN:install.packages("hqreg") the latest version (requiredevtools):install_github("CY-dev/hqreg") ...
We will consider nonparametric regression estimation in Section 2.6, and we will develop some new nonparametric estimates of the conditional mean based on local Gaussian approximations in Chapter 12. Show moreView chapter Review article Shrinkage priors for Bayesian penalized regression Journal of ...
L1-penalized quantile regression in high-dimensional sparse models. Ann Stat. 2011;39(1):82–130. 56. Xue X, Xie X, Strickler HD. A censored quantile regression approach for the analysis of time to event data. Stat Methods Med Res. 2018;27(3): 955–65. 57. Zhanfeng W, Yaohua W,...
Consistencies and rates of convergence of jump-penalized least squares estimators Ann. Statist. (2009) P.J. Brockwell et al. On the existence of stationary threshold autoregressive moving-average processes J. Time Ser. Anal. (1992) K.S. Chan Consistency and limiting distribution of the least sq...
Generalized linear mixed models (GLMMs), typically used for analyzing correlated data, can also be used for smoothing by considering the knot coefficients from a regression spline as random effects. The resulting models are called semiparametric mixed mo
Quantile regression (QR) is a natural alternative for depicting the impact of covariates on the conditional distributions of a outcome variable instead of the mean. In this paper, we investigate Bayesian regularized QR for the linear models with autoregressive errors. LASSO-penalized type priors are...
SamplingLassoSLQRWith the quantile regression methods successfully applied in various applications, we often need to tackle with the big dataset with thousands of variables and millions of observations. In this paper, we focus on the variable selection aspect of penalized quantile regression, and ...
quantile regressionskewed Laplace distributionRecently, variable selection by penalized likelihood has attracted much research interest. In this paper, we propose adaptive Lasso quantile regression (BALQR) from a Bayesian perspective. The method extends the Bayesian Lasso quantile regression by allowing ...