Fast Lasso‐type safe screening for Fine‐Gray competing risks model with ultrahigh dimensional covariates
Sign Up to like & getrecommendations! Published in 2022 at "Statistics in Medicine"
DOI: 10.1002/sim.9545
Abstract: The Fine‐Gray proportional sub‐distribution hazards (PSH) model is among the most popular regression model for competing risks time‐to‐event data. This article develops a fast safe feature elimination method, named PSH‐SAFE, for fitting the penalized Fine‐Gray… read more here.
Keywords: psh safe; fine gray; model; competing risks ... See more keywords
Robust composite weighted quantile screening for ultrahigh dimensional discriminant analysis
Sign Up to like & getrecommendations! Published in 2020 at "Metrika"
DOI: 10.1007/s00184-019-00758-x
Abstract: This paper is concerned with feature screening for the ultrahigh dimensional discriminant analysis. A new feature screening procedure based on the conditional quantile is proposed. The proposed procedure has some desirable features. First, it is… read more here.
Keywords: ultrahigh dimensional; dimensional discriminant; screening ultrahigh; discriminant analysis ... See more keywords
Stable feature screening for ultrahigh dimensional data
Sign Up to like & getrecommendations! Published in 2019 at "Journal of the Korean Statistical Society"
DOI: 10.1016/j.jkss.2018.11.003
Abstract: Abstract This paper is concerned with the stable feature screening for the ultrahigh dimensional data. To deal with the ultrahigh dimensional data problem and screen the important features, a set-averaging measurement is proposed. The model… read more here.
Keywords: screening ultrahigh; ultrahigh dimensional; feature screening; dimensional data ... See more keywords
Feature screening of quadratic inference functions for ultrahigh dimensional longitudinal data
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Statistical Computation and Simulation"
DOI: 10.1080/00949655.2020.1783666
Abstract: This paper is concerned with feature screening for the ultrahigh dimensional additive models with longitudinal data. The proposed method utilizes the quadratic inference functions to construct the marginal screening measurement, which takes the within-subject correlation… read more here.
Keywords: longitudinal data; quadratic inference; feature screening; inference functions ... See more keywords
Ultrahigh-dimensional sufficient dimension reduction for censored data with measurement error in covariates
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Applied Statistics"
DOI: 10.1080/02664763.2020.1856352
Abstract: In this paper, we consider the ultrahigh-dimensional sufficient dimension reduction (SDR) for censored data and measurement error in covariates. We first propose the feature screening procedure bas... read more here.
Keywords: data measurement; sufficient dimension; dimensional sufficient; dimension reduction ... See more keywords
Computationally scalable regression modeling for ultrahigh-dimensional omics data with ParProx.
Sign Up to like & getrecommendations! Published in 2021 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbab256
Abstract: Statistical analysis of ultrahigh-dimensional omics scale data has long depended on univariate hypothesis testing. With growing data features and samples, the obvious next step is to establish multivariable association analysis as a routine method to… read more here.
Keywords: dimensional omics; regression; analysis; omics data ... See more keywords
An Ultrahigh-Dimensional Mapping Model of High-order Epistatic Networks for Complex Traits
Sign Up to like & getrecommendations! Published in 2018 at "Current Genomics"
DOI: 10.2174/1389202919666171218162210
Abstract: Background: Genetic interactions involving more than two loci have been thought to affect quantitatively inherited traits and diseases more pervasively than previously appreciated. However, the detection of such high-order interactions to chart a complete portrait… read more here.
Keywords: mapping; order epistatic; model; high order ... See more keywords
Quantile-Composited Feature Screening for Ultrahigh-Dimensional Data
Sign Up to like & getrecommendations! Published in 2023 at "Mathematics"
DOI: 10.3390/math11102398
Abstract: Ultrahigh-dimensional grouped data are frequently encountered by biostatisticians working on multi-class categorical problems. To rapidly screen out the null predictors, this paper proposes a quantile-composited feature screening procedure. The new method first transforms the continuous… read more here.
Keywords: quantile composited; composited feature; screening ultrahigh; feature screening ... See more keywords
Efficient feature screening for ultrahigh-dimensional varying coefficient models
Sign Up to like & getrecommendations! Published in 2017 at "Statistics and Its Interface"
DOI: 10.4310/sii.2017.v10.n3.a5
Abstract: Feature screening in ultrahigh-dimensional varying coefficient models is a crucial statistical problem in economics, genomics, etc. Current methods not only suffer from circumstances when the models involve multiple index variables or group predictor variables, but… read more here.
Keywords: dimensional varying; varying coefficient; ultrahigh dimensional; coefficient models ... See more keywords
Forward Additive Regression for Ultrahigh Dimensional Nonparametric Additive Models
Sign Up to like & getrecommendations! Published in 2020 at "Statistica Sinica"
DOI: 10.5705/ss.202017.0083
Abstract: Ultrahigh dimensional data are collected in many scientific fields where the predictor dimension is often much higher than the sample size. To reduce the ultrahigh dimensionality effectively, many marginal screening approaches are developed. However, existing… read more here.
Keywords: forward additive; regression; ultrahigh dimensional; additive models ... See more keywords