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High-dimensional survival analysis: Methods and applications
In the era of precision medicine, time-to-event outcomes such as time to death or
progression are routinely collected, along with high-throughput covariates. These high …
progression are routinely collected, along with high-throughput covariates. These high …
Semiparametric model averaging prediction for lifetime data via hazards regression
Forecasting survival risks for time-to-event data is an essential task in clinical research.
Practitioners often rely on well-structured statistical models to make predictions for patient …
Practitioners often rely on well-structured statistical models to make predictions for patient …
A selective overview of feature screening methods with applications to neuroimaging data
In neuroimaging studies, regression models are frequently used to identify the association of
the imaging features and clinical outcome, where the number of imaging features (eg …
the imaging features and clinical outcome, where the number of imaging features (eg …
Cox regression increases power to detect genotype-phenotype associations in genomic studies using the electronic health record
Background The growth of DNA biobanks linked to data from electronic health records
(EHRs) has enabled the discovery of numerous associations between genomic variants and …
(EHRs) has enabled the discovery of numerous associations between genomic variants and …
Robust feature screening for ultra-high dimensional right censored data via distance correlation
Ultra-high dimensional data with right censored survival times are frequently collected in
large-scale biomedical studies, for which feature screening has become an indispensable …
large-scale biomedical studies, for which feature screening has become an indispensable …
Integrated powered density: Screening ultrahigh dimensional covariates with survival outcomes
Modern biomedical studies have yielded abundant survival data with high-throughput
predictors. Variable screening is a crucial first step in analyzing such data, for the purpose of …
predictors. Variable screening is a crucial first step in analyzing such data, for the purpose of …
[HTML][HTML] Forward regression for Cox models with high-dimensional covariates
Forward regression, a classical variable screening method, has been widely used for model
building when the number of covariates is relatively low. However, forward regression is …
building when the number of covariates is relatively low. However, forward regression is …
Quantile forward regression for high-dimensional survival data
Despite the urgent need for an effective prediction model tailored to individual interests,
existing models have mainly been developed for the mean outcome, targeting average …
existing models have mainly been developed for the mean outcome, targeting average …
High-dimensional variable selection with heterogeneous signals: A precise asymptotic perspective
High-dimensional variable selection with heterogeneous signals: A precise asymptotic
perspective Page 1 Bernoulli 31(2), 2025, 1206–1229 https://doi.org/10.3150/24-BEJ1767 …
perspective Page 1 Bernoulli 31(2), 2025, 1206–1229 https://doi.org/10.3150/24-BEJ1767 …
Partition-based ultrahigh-dimensional variable screening
Traditional variable selection methods are compromised by overlooking useful information
on covariates with similar functionality or spatial proximity, and by treating each covariate …
on covariates with similar functionality or spatial proximity, and by treating each covariate …