A Frisch-Newton algorithm for sparse quantile regression, Acta Mathematicae Applicatae Sinica (English series) 21: 225-236.Koenker, R. and P. Ng (2005). "A Frisch-Newton Algorithm for Sparse Quantile Regression." Acta Mathematicae Applicatae Sinica (English Series), 21(2): 225-236.Koenker, ...
Sparse penalized quantile regression is a useful tool for variable selection, robust estimation, and heteroscedasticity detection in high-dimensional data analysis. The computational issue of the sparse penalized quantile regression has not yet been fully resolved in the literature, due to nonsmoothness ...
53 国际基础科学大会-A quasi-dynamic system representation of multiple nonlinear regression 1:01:30 国际基础科学大会-Locally symmetric varieties with moduli interpretation-Chenglong Yu 1:01:43 国际基础科学大会-Equivariant K-theory realization of the affine i-quantum group 1:04:02 国际基础科学大会-A...
We consider median regression and, more generally, a possibly infinite collection of quantile regressions in high-dimensional sparse models. In these models the overall number of regressors $p$ is very large, possibly larger than the sample size $n$, but
Quantile functional linear regression was previously studied using functional principal component analysis. Here we consider the alternative penalized esti... LA Rui,WLB C,ZZ B,... - 《Journal of Statistical Planning & Inference》 被引量: 0发表: 2021年 Optimal prediction of quantile functional line...
Elastic net penalized quantile regression model 2021, Journal of Computational and Applied Mathematics Show abstract Machine learning approaches in microbiome research: challenges and best practices 2023, Frontiers in Microbiology Robust linear regression for high-dimensional data: An overview 2021, Wiley Int...
Predictivity: assessed through the AUROC for classification tasks or the RMSE for regression tasks Model performances were evaluated over 100 random repetitions using a repeated five-fold or Monte Carlo CV strategy. Sparse, reliable biomarker discovery from single-omic data ...
To detect such outliers, we used a quantile-quantile plot to visualize the observed distribution of gene effect variances compared to a theoretical normal distribution. We drew a cutoff that separated high variance genes from the remaining (variance > 3), resulting in 304 gene effects over 31 ...
In this paper, we propose a multivariate quantile regression method which enables localized analysis on conditional quantiles and global comovement analysis on conditional ranges for high-dimensional data. The proposed method, hereafter referred to as FActorisable Sparse Tail Event Curves, or FASTEC for...
Because it formed such a steady long-lived predictive relationship, a researcher could intuit this variable and then estimate its quality with an ordinary least-squares (OLS) regression: rt− = αˆ + βˆ · xt−( +1) + εt− ∈ {0, . . . , L − 1} (1) Above, rt ...