Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

An Adaptive Ridge Procedure for L0 Regularization.

ABSTRACT: Penalized selection criteria like AIC or BIC are among the most popular methods for variable selection. Their theoretical properties have been studied intensively and are well understood, but making use of them in case of high-dimensional data is difficult due to the non-convex optimization problem induced by L0 penalties. In this paper we introduce an adaptive ridge procedure (AR), where iteratively weighted ridge problems are solved whose weights are updated in such a way that the procedure converges towards selection with L0 penalties. After introducing AR its specific shrinkage properties are studied in the particular case of orthogonal linear regression. Based on extensive simulations for the non-orthogonal case as well as for Poisson regression the performance of AR is studied and compared with SCAD and adaptive LASSO. Furthermore an efficient implementation of AR in the context of least-squares segmentation is presented. The paper ends with an illustrative example of applying AR to analyze GWAS data.

SUBMITTER: Frommlet F

PROVIDER: S-EPMC4743917 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

An Adaptive Ridge Procedure for L0 Regularization.

Frommlet Florian F Nuel Grégory G

PloS one 20160205 2

Penalized selection criteria like AIC or BIC are among the most popular methods for variable selection. Their theoretical properties have been studied intensively and are well understood, but making use of them in case of high-dimensional data is difficult due to the non-convex optimization problem induced by L0 penalties. In this paper we introduce an adaptive ridge procedure (AR), where iteratively weighted ridge problems are solved whose weights are updated in such a way that the procedure co ...[more]

PMID: 26849123

Similar Datasets

Connectivity-informed adaptive regularization for generalized outcomes.

Project description:One of the challenging problems in neuroimaging is the principled incorporation of information from different imaging modalities. Data from each modality are frequently analyzed separately using, for instance, dimensionality reduction techniques, which result in a loss of mutual information. We propose a novel regularization method, generalized ridgified Partially Empirical Eigenvectors for Regression (griPEER), to estimate associations between the brain structure features and a scalar outcome within the generalized linear regression framework. griPEER improves the regression coefficient estimation by providing a principled approach to use external information from the structural brain connectivity. Specifically, we incorporate a penalty term, derived from the structural connectivity Laplacian matrix, in the penalized generalized linear regression. In this work, we address both theoretical and computational issues and demonstrate the robustness of our method despite incomplete information about the structural brain connectivity. In addition, we also provide a significance testing procedure for performing inference on the estimated coefficients. Finally, griPEER is evaluated both in extensive simulation studies and using clinical data to classify HIV+ and HIV- individuals.

| S-EPMC8730330 | biostudies-literature

Sparse spatially clustered coefficient model via adaptive regularization.

Project description:Large spatial datasets with many spatial covariates have become ubiquitous in many fields in recent years. A question of interest is to identify which covariates are likely to influence a spatial response, and whether and how the effects of these covariates vary across space, including potential abrupt changes from region to region. To solve this question, a new efficient regularized spatially clustered coefficient (RSCC) regression approach is proposed, which could achieve variable selection and identify latent spatially heterogeneous covariate effects with clustered patterns simultaneously. By carefully designing the regularization term of RSCC as a chain graph guided fusion penalty plus a group lasso penalty, the RSCC model is computationally efficient for large spatial datasets while still achieving the theoretical guarantees for estimation. RSCC also adopts the idea of adaptive learning to allow for adaptive weights and adaptive graphs in its regularization terms and further improves the estimation performance. RSCC is applied to study the acceptance of COVID-19 vaccines using county-level data in the United States and discover the determinants of vaccination acceptance with varying effects across counties, revealing important within-state and across-state spatially clustered patterns of covariates effects.

| S-EPMC9335734 | biostudies-literature

Adaptive ridge regression for rare variant detection.

Project description:It is widely believed that both common and rare variants contribute to the risks of common diseases or complex traits and the cumulative effects of multiple rare variants can explain a significant proportion of trait variances. Advances in high-throughput DNA sequencing technologies allow us to genotype rare causal variants and investigate the effects of such rare variants on complex traits. We developed an adaptive ridge regression method to analyze the collective effects of multiple variants in the same gene or the same functional unit. Our model focuses on continuous trait and incorporates covariate factors to remove potential confounding effects. The proposed method estimates and tests multiple rare variants collectively but does not depend on the assumption of same direction of each rare variant effect. Compared with the Bayesian hierarchical generalized linear model approach, the state-of-the-art method of rare variant detection, the proposed new method is easy to implement, yet it has higher statistical power. Application of the new method is demonstrated using the well-known data from the Dallas Heart Study.

| S-EPMC3429469 | biostudies-literature

Broken adaptive ridge regression and its asymptotic properties.

Project description:This paper studies the asymptotic properties of a sparse linear regression estimator, referred to as broken adaptive ridge (BAR) estimator, resulting from an L 0-based iteratively reweighted L 2 penalization algorithm using the ridge estimator as its initial value. We show that the BAR estimator is consistent for variable selection and has an oracle property for parameter estimation. Moreover, we show that the BAR estimator possesses a grouping effect: highly correlated covariates are naturally grouped together, which is a desirable property not known for other oracle variable selection methods. Lastly, we combine BAR with a sparsity-restricted least squares estimator and give conditions under which the resulting two-stage sparse regression method is selection and estimation consistent in addition to having the grouping property in high- or ultrahigh-dimensional settings. Numerical studies are conducted to investigate and illustrate the operating characteristics of the BAR method in comparison with other methods.

| S-EPMC6430210 | biostudies-literature

A Regularization-Based Adaptive Test for High-Dimensional Generalized Linear Models.

Project description:In spite of its urgent importance in the era of big data, testing high-dimensional parameters in generalized linear models (GLMs) in the presence of high-dimensional nuisance parameters has been largely under-studied, especially with regard to constructing powerful tests for general (and unknown) alternatives. Most existing tests are powerful only against certain alternatives and may yield incorrect Type I error rates under high-dimensional nuisance parameter situations. In this paper, we propose the adaptive interaction sum of powered score (aiSPU) test in the framework of penalized regression with a non-convex penalty, called truncated Lasso penalty (TLP), which can maintain correct Type I error rates while yielding high statistical power across a wide range of alternatives. To calculate its p-values analytically, we derive its asymptotic null distribution. Via simulations, its superior finite-sample performance is demonstrated over several representative existing methods. In addition, we apply it and other representative tests to an Alzheimer's Disease Neuroimaging Initiative (ADNI) data set, detecting possible gene-gender interactions for Alzheimer's disease. We also put R package "aispu" implementing the proposed test on GitHub.

| S-EPMC7425805 | biostudies-literature

Spatially Adaptive Regularization in Total Field Inversion for Quantitative Susceptibility Mapping.

Project description:Adaptive Total Field Inversion is described for quantitative susceptibility mapping (QSM) reconstruction from total field data through a spatially adaptive suppression of shadow artifacts through spatially adaptive regularization. The regularization for shadow suppression consists of penalizing low-frequency components of susceptibility in regions of small susceptibility contrasts as estimated by R2∗ derived signal intensity. Compared with a conventional local field method and two previously proposed regularized total field inversion methods, improvements were demonstrated in phantoms and subjects without and with hemorrhages. This algorithm, named TFIR, demonstrates the lowest error in numerical and gadolinium phantom datasets. In COSMOS data, TFIR performs well in matching ground truth in high-susceptibility regions. For patient data, TFIR comes close to meeting the quality of the reference local field method and outperforms other total field techniques in both clinical scores and shadow reduction.

| S-EPMC7522736 | biostudies-literature

Alveolar ridge dimensional changes following ridge preservation procedure with novel devices: Part 1--CBCT linear analysis in non-human primate model.

Project description:AimThis study sought to investigate dimensional changes to the alveolar bone following extraction and application of novel devices used for obturation of socket orifice (socket cap) and space maintenance in sockets with facial dehiscence (socket cage).Material and methodsSix Macaca fascicularis had six teeth each removed according to the following intervention groups (groups A-C intact alveolar bone; D-E facial dehiscence): negative control (A); socket obturated with cap (B); filled with anorganic bovine bone mineral (ABBM) + socket cap (C); dehiscence negative control (D); socket cap + socket cage (E); ABBM + socket cap + socket cage (F). Serial CBCT scans at preoperatively, 6 and 12 weeks following intervention were compared to quantify linear alveolar bone alterations.ResultsWithout therapeutic intervention, intact sockets exhibited significant reduction in width at the crestal 2 mm of the ridge crest within 6 weeks. Compared with the negative control sites which lost up to 52% of crestal bone width, sites treated with socket cap + ABBM lost at most 4% of bone width at the crestal 2 mm. Similar results were seen in the dehiscence groups, with the combination of socket cap + socket cage + ABBM maintaining the greatest socket width and height dimensions.ConclusionsResults from the current non-human primate study suggest that the socket cap and socket cage devices, when used in conjunction with xenograft proved effective in minimizing post-extraction socket width loss and height seen in both intact sockets and sockets with facial dehiscence defects.

| S-EPMC5024076 | biostudies-literature

Joint Adaptive Mean-Variance Regularization and Variance Stabilization of High Dimensional Data.

Project description:The paper addresses a common problem in the analysis of high-dimensional high-throughput "omics" data, which is parameter estimation across multiple variables in a set of data where the number of variables is much larger than the sample size. Among the problems posed by this type of data are that variable-specific estimators of variances are not reliable and variable-wise tests statistics have low power, both due to a lack of degrees of freedom. In addition, it has been observed in this type of data that the variance increases as a function of the mean. We introduce a non-parametric adaptive regularization procedure that is innovative in that : (i) it employs a novel "similarity statistic"-based clustering technique to generate local-pooled or regularized shrinkage estimators of population parameters, (ii) the regularization is done jointly on population moments, benefiting from C. Stein's result on inadmissibility, which implies that usual sample variance estimator is improved by a shrinkage estimator using information contained in the sample mean. From these joint regularized shrinkage estimators, we derived regularized t-like statistics and show in simulation studies that they offer more statistical power in hypothesis testing than their standard sample counterparts, or regular common value-shrinkage estimators, or when the information contained in the sample mean is simply ignored. Finally, we show that these estimators feature interesting properties of variance stabilization and normalization that can be used for preprocessing high-dimensional multivariate data. The method is available as an R package, called 'MVR' ('Mean-Variance Regularization'), downloadable from the CRAN website.

| S-EPMC3375876 | biostudies-literature

Variable selection for recurrent event data with broken adaptive ridge regression.

Project description:Recurrent event data occur in many areas such as medical studies and social sciences and a great deal of literature has been established for their analysis. On the other hand, only limited research exists on the variable selection for recurrent event data, and the existing methods can be seen as direct generalizations of the available penalized procedures for linear models and may not perform as well as expected. This article discusses simultaneous parameter estimation and variable selection and presents a new method with a new penalty function, which will be referred to as the broken adaptive ridge regression approach. In addition to the establishment of the oracle property, we also show that the proposed method has the clustering or grouping effect when covariates are highly correlated. Furthermore, a numerical study is performed and indicates that the method works well for practical situations and can outperform existing methods. An application is provided.

| S-EPMC7523880 | biostudies-literature

Types of devices used in ridge split procedure for alveolar bone expansion: A systematic review.

Project description:The aim of this systematic review was to evaluate instrumentation procedures of the alveolar ridge expansion technique (ARST) with or without Guided Bone Regeneration (GBR) and to identify the most used instruments for successful outcome. An electronic as well as manual literature search was conducted in several databases including Medline, Embase, and Cochrane Central Register of Controlled Trials, for articles written in English up to September 2016. The question in focus was to identify the type of device for ridge expansion that is most frequently used and provides adequate bone expansion and implant success rate. To meet the inclusion criteria, the studies were analysed for the following parameters: prospective or retrospective studies, cohort or case studies/series, cases with 5 or more human subjects, type of device used for surgery, location of defect, and minimum follow up period. The frequency of osteotome usage in this study was approximately 65%, and on average, the implant success was 97%. The motorized expanders and ultrasonic surgery system are easier to use and cause less trauma to the bone compared to the traditional/conventional instruments like mallets and osteotomes. However, their cost is a limiting factor; hence, osteotomes remain a popular mode of instrumentation.

| S-EPMC5521746 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data