Model free feature screening for ultrahigh dimensional data with responses missing at random
From MaRDI portal
Publication:1658537
DOI10.1016/j.csda.2016.08.008zbMath1466.62125OpenAlexW2515165565MaRDI QIDQ1658537
Zhi Liu, Peng Lai, Yi Wan, Yi-Ming Liu
Publication date: 14 August 2018
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2016.08.008
Related Items (24)
Statistical inference for nonignorable missing-data problems: a selective review ⋮ Variable screening with missing covariates: a discussion of ‘statistical inference for nonignorable missing data problems: a selective review’ by Niansheng Tang and Yuanyuan Ju ⋮ Nonparametric independence feature screening for ultrahigh-dimensional missing data ⋮ Censored mean variance sure independence screening for ultrahigh dimensional survival data ⋮ A scalable surrogate \(L_0\) sparse regression method for generalized linear models with applications to large scale data ⋮ Ultrahigh dimensional feature screening via projection ⋮ Conditional distance correlation sure independence screening for ultra-high dimensional survival data ⋮ Robust feature screening for ultra-high dimensional right censored data via distance correlation ⋮ Model free feature screening with dependent variable in ultrahigh dimensional binary classification ⋮ Joint model-free feature screening for ultra-high dimensional semi-competing risks data ⋮ Robust Feature Screening via Distance Correlation for Ultrahigh Dimensional Data With Responses Missing at Random ⋮ Feature screening under missing indicator imputation with non-ignorable missing response ⋮ Regularized quantile regression for ultrahigh-dimensional data with nonignorable missing responses ⋮ Robust model-free feature screening for ultrahigh dimensional surrogate data ⋮ Unnamed Item ⋮ Feature screening in ultrahigh-dimensional partially linear models with missing responses at random ⋮ Stable feature screening for ultrahigh dimensional data ⋮ The fused Kolmogorov-Smirnov screening for ultra-high dimensional semi-competing risks data ⋮ Feature screening for ultrahigh dimensional categorical data with covariates missing at random ⋮ A nonparametric feature screening method for ultrahigh-dimensional missing response ⋮ An efficient algorithm for joint feature screening in ultrahigh-dimensional Cox's model ⋮ Broken adaptive ridge regression and its asymptotic properties ⋮ Quantile screening for ultra-high-dimensional heterogeneous data conditional on some variables ⋮ Sure independence screening in the presence of missing data
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Model-Free Feature Screening for Ultrahigh-Dimensional Data
- Sure independence screening in generalized linear models with NP-dimensionality
- The Adaptive Lasso and Its Oracle Properties
- Robust rank correlation based screening
- Nonconcave penalized likelihood with a diverging number of parameters.
- Spline estimation and variable selection for single-index prediction models with diverging number of index parameters
- Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models
- Approximation Theorems of Mathematical Statistics
- Efficient and Doubly Robust Imputation for Covariate-Dependent Missing Responses
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Feature Screening via Distance Correlation Learning
- Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis
- The Kolmogorov filter for variable screening in high-dimensional binary classification
- Semiparametric Regression Analysis With Missing Response at Random
- A Generalization of Sampling Without Replacement From a Finite Universe
This page was built for publication: Model free feature screening for ultrahigh dimensional data with responses missing at random
