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 …
Mediation analysis for high-dimensional mediators and outcomes with an application to multimodal imaging data
Multimodal neuroimaging data have attracted increasing attention for brain research. An
integrated analysis of multimodal neuroimaging data and behavioral or clinical …
integrated analysis of multimodal neuroimaging data and behavioral or clinical …
An in-depth association analysis of genetic variants within nicotine-related loci: Meeting in middle of GWAS and genetic fine-map**
In the last two decades of Genome-wide association studies (GWAS), nicotine-dependence-
related genetic loci (eg, nicotinic acetylcholine receptor–nAChR subunit genes) are among …
related genetic loci (eg, nicotinic acetylcholine receptor–nAChR subunit genes) are among …
High-dimension to high-dimension screening for detecting genome-wide epigenetic and noncoding RNA regulators of gene expression
Motivation The advancement of high-throughput technology characterizes a wide variety of
epigenetic modifications and noncoding RNAs across the genome involved in disease …
epigenetic modifications and noncoding RNAs across the genome involved in disease …
Covariance matrix estimation for high-throughput biomedical data with interconnected communities
Estimating a covariance matrix is central to high-dimensional data analysis. Empirical
analyses of high-dimensional biomedical data, including genomics, proteomics …
analyses of high-dimensional biomedical data, including genomics, proteomics …
[HTML][HTML] Prior knowledge guided ultra-high dimensional variable screening with application to neuroimaging data
J He, J Kang - Statistica Sinica, 2022 - ncbi.nlm.nih.gov
Variable screening is a powerful and efficient tool for dimension reduction under ultrahigh
dimensional settings. However, most existing methods overlook useful prior knowledge in …
dimensional settings. However, most existing methods overlook useful prior knowledge in …
Spectral Analysis of Electrophysiological Data
9.6. 3 Univariate SLEX Model 9.6. 4 Dyadic Aggregated Autoregressive Model 9.7
Multivariate Stationary Spectrum 9.7. 1 Multivariate Granger Causality 9.7. 2 Cross …
Multivariate Stationary Spectrum 9.7. 1 Multivariate Granger Causality 9.7. 2 Cross …
Spatial Modeling of Imaging and Electrophysiological Data
Imaging and electrophysiological data are frequently employed in the evaluation of epilepsy.
By measuring data such as electrophysiological activity via electroencephalography (EEG) …
By measuring data such as electrophysiological activity via electroencephalography (EEG) …
Block-diagonal precision matrix regularization for ultra-high dimensional data
A method that estimates the precision matrix of multiple variables in the extreme scope of
“ultrahigh dimension” and “small sample-size” is proposed. Initially, a covariance column …
“ultrahigh dimension” and “small sample-size” is proposed. Initially, a covariance column …
Robust distance correlation for variable screening
High-dimensional data are commonly seen in modern statistical applications, variable
selection methods play indispensable roles in identifying the critical features for scientific …
selection methods play indispensable roles in identifying the critical features for scientific …