Search results for " sparsity"

showing 3 items of 3 documents

An Extension of the DgLARS Method to High-Dimensional Relative Risk Regression Models

2020

In recent years, clinical studies, where patients are routinely screened for many genomic features, are becoming more common. The general aim of such studies is to find genomic signatures useful for treatment decisions and the development of new treatments. However, genomic data are typically noisy and high dimensional, not rarely outstripping the number of patients included in the study. For this reason, sparse estimators are usually used in the study of high-dimensional survival data. In this paper, we propose an extension of the differential geometric least angle regression method to high-dimensional relative risk regression models.

Clustering high-dimensional dataComputer sciencedgLARS Gene expression data High-dimensional data Relative risk regression models Sparsity · Survival analysisLeast-angle regressionRelative riskStatisticsEstimatorRegression analysisExtension (predicate logic)High dimensionalSettore SECS-S/01 - StatisticaSurvival analysis
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cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values

2023

Sparse graphical models have revolutionized multivariate inference. With the advent of high-dimensional multivariate data in many applied fields, these methods are able to detect a much lower-dimensional structure, often represented via a sparse conditional independence graph. There have been numerous extensions of such methods in the past decade. Many practical applications have additional covariates or suffer from missing or censored data. Despite the development of these extensions of sparse inference methods for graphical models, there have been so far no implementations for, e.g., conditional graphical models. Here we present the general-purpose package cglasso for estimating sparse co…

Statistics and Probabilityconditional Gaussian graphical modelscglasso conditional Gaussian graphical models glasso high-dimensionality sparsity censoring missing dataglassosparsityhigh-dimensionalityconditional Gaussian graphical models glasso high-dimensionality sparsity censoring missing datacglassomissing datacensoringStatistics Probability and UncertaintySettore SECS-S/01 - StatisticaSoftware
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Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data with a Phase Sparsity Constraint

2020

Canonical polyadic decomposition (CPD) of multi-subject complex-valued fMRI data can be used to provide spatially and temporally shared components among groups with both magnitude and phase information. However, the CPD model is not well formulated due to the large subject variability in the spatial and temporal modalities, as well as the high noise level in complex-valued fMRI data. Considering that the shift-invariant CPD can model temporal variability across subjects, we propose to further impose a phase sparsity constraint on the shared spatial maps to denoise the complex-valued components and to model the inter-subject spatial variability as well. More precisely, subject-specific time …

complex-valued fMRI dataComputer sciencespatiotemporal constraintscomputer.software_genrecanonical polyadic decomposition (CPD)030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicinetoiminnallinen magneettikuvausVoxelshift-invariantImage Processing Computer-AssistedmedicineHumansTensorElectrical and Electronic EngineeringInvariant (mathematics)Radiological and Ultrasound Technologymedicine.diagnostic_testsignaalinkäsittelyBrainComplex valuedsignaalianalyysiSignal Processing Computer-Assistedsource phase sparsityMagnetic Resonance ImagingComputer Science ApplicationsNorm (mathematics)Frequency domainSpatial variabilityFunctional magnetic resonance imagingAlgorithmcomputerAlgorithmsSoftware
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