Browse
Submit Data
Databases
API
Help

Dataset Information

20 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

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:AIM:This 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 METHODS:Six 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. RESULTS:Without 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. CONCLUSIONS:Results 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

Adaptive propensity score procedure improves matching in prospective observational trials.

Project description:BACKGROUND:Randomized controlled trials are the gold-standard for clinical trials. However, randomization is not always feasible. In this article we propose a prospective and adaptive matched case-control trial design assuming that a control group already exists. METHODS:We propose and discuss an interim analysis step to estimate the matching rate using a resampling step followed by a sample size recalculation. The sample size recalculation is based on the observed mean resampling matching rate. We applied our approach in a simulation study and to a real data set to evaluate the characteristics of the proposed design and to compare the results to a naive approach. RESULTS:The proposed design achieves at least 10% higher matching rate than the naive approach at final analysis, thus providing a better estimation of the true matching rate. A good choice for the interim analysis seems to be a fraction of around [Formula: see text] to [Formula: see text] of the control patients. CONCLUSION:The proposed resampling step in a prospective matched case-control trial design leads to an improved estimate of the final matching rate and, thus, to a gain in power of the approach due to sensible sample size recalculation.

| S-EPMC6636117 | biostudies-literature

qPR: An adaptive partial-report procedure based on Bayesian inference.

Project description:Iconic memory is best assessed with the partial report procedure in which an array of letters appears briefly on the screen and a poststimulus cue directs the observer to report the identity of the cued letter(s). Typically, 6-8 cue delays or 600-800 trials are tested to measure the iconic memory decay function. Here we develop a quick partial report, or qPR, procedure based on a Bayesian adaptive framework to estimate the iconic memory decay function with much reduced testing time. The iconic memory decay function is characterized by an exponential function and a joint probability distribution of its three parameters. Starting with a prior of the parameters, the method selects the stimulus to maximize the expected information gain in the next test trial. It then updates the posterior probability distribution of the parameters based on the observer's response using Bayesian inference. The procedure is reiterated until either the total number of trials or the precision of the parameter estimates reaches a certain criterion. Simulation studies showed that only 100 trials were necessary to reach an average absolute bias of 0.026 and a precision of 0.070 (both in terms of probability correct). A psychophysical validation experiment showed that estimates of the iconic memory decay function obtained with 100 qPR trials exhibited good precision (the half width of the 68.2% credible interval = 0.055) and excellent agreement with those obtained with 1,600 trials of the conventional method of constant stimuli procedure (RMSE = 0.063). Quick partial-report relieves the data collection burden in characterizing iconic memory and makes it possible to assess iconic memory in clinical populations.

| S-EPMC5019336 | 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