Feature screening for ultrahigh-dimensional survival data when failure indicators are missing at random
Sign Up to like & getrecommendations! Published in 2019 at "Statistical Papers"
DOI: 10.1007/s00362-019-01128-5
Abstract: In modern statistical applications, the dimension of covariates can be much larger than the sample size, and extensive research has been done on screening methods which can effectively reduce the dimensionality. However, the existing feature… read more here.
Keywords: missing random; feature screening; failure indicators; indicators missing ... 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
Grouped feature screening for ultra-high dimensional data for the classification model
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Statistical Computation and Simulation"
DOI: 10.1080/00949655.2021.1981901
Abstract: Existing model-free ultra-high-dimensional feature screening methods mainly focus on the individual covariate. However, many variables have a community structure, such as grouped covariates in which all variates have high correlation and some associations in one… read more here.
Keywords: ultra high; feature; grouped feature; model ... See more keywords
Class-specific Joint Feature Screening in Ultrahigh-dimensional Mixture Regression*
Sign Up to like & getrecommendations! Published in 2025 at "Journal of the American Statistical Association"
DOI: 10.1080/01621459.2025.2468011
Abstract: Abstract Finite mixture of regression models are ubiquitous for analyzing complex data. They aim to detect heterogeneity in the effects of a set of features on a response over a finite number of latent classes.… read more here.
Keywords: class specific; screening ultrahigh; ultrahigh dimensional; mixture regression ... See more keywords
Variation of conditional mean and its application in ultrahigh dimensional feature screening
Sign Up to like & getrecommendations! Published in 2024 at "Communications in Statistics - Theory and Methods"
DOI: 10.1080/03610926.2024.2310690
Abstract: Abstract. A new metric, called variation of conditional mean (VCM), is proposed to measure the dependence of conditional mean of a response variable on a predictor variable. The VCM has several appealing merits. It equals… read more here.
Keywords: variation conditional; feature screening; conditional mean;
Feature screening filter for high dimensional survival data in the reproducing kernel Hilbert space
Sign Up to like & getrecommendations! Published in 2024 at "Communications in Statistics - Theory and Methods"
DOI: 10.1080/03610926.2024.2413846
Abstract: Abstract We consider the issue of screening uninformative variables associate with survival outcome in ultra high-dimensional situation. specifically, we focus on Hilbert-Schmits Independent criteria (HSIC) statistic defined in the Reproducing kernel Hilbert Space(RKHS) and use… read more here.
Keywords: reproducing kernel; kernel hilbert; hilbert space; high dimensional ... See more keywords
Feature screening with latent responses.
Sign Up to like & getrecommendations! Published in 2022 at "Biometrics"
DOI: 10.1111/biom.13658
Abstract: A novel feature screening method is proposed to examine the correlation between latent responses and potential predictors in ultrahigh dimensional data analysis. First, a confirmatory factor analysis (CFA) model is used to characterize latent responses… read more here.
Keywords: screening latent; feature screening; screening procedure; latent responses ... 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