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Optimized application of penalized regression methods to diverse genomic data

Levi Waldron, Melania Pintilie, Ming-Sound Tsao, Frances A. Shepherd, Curtis Huttenhower, Igor Jurisica
2011 Computer applications in the biosciences : CABIOS  
Motivation: Penalized regression methods have been adopted widely for high-dimensional feature selection and prediction in many bioinformatic and biostatistical contexts.  ...  We found that a 2D tuning of the Elastic Net penalties was necessary to avoid mimicking the performance of LASSO or Ridge regression.  ...  CONCLUSIONS In this study, we outline and implement guidelines to optimize the performance of penalized regression, as assessed by parameter tuning, variable selection and prediction in independent highdimensional  ... 
doi:10.1093/bioinformatics/btr591 pmid:22156367 pmcid:PMC3232376 fatcat:w54y54pcabcwngccjn4ebaxyvi

Penalized regression for interval-censored times of disease progression: Selection of HLA markers in psoriatic arthritis

Ying Wu, Richard J. Cook
2015 Biometrics  
Penalized regression techniques (e.g. LASSO, adaptive LASSO and SCAD) are adapted to handle interval-censored time of disease progression.  ...  We consider the problem of selecting important prognostic biomarkers from a large set of candidates when disease progression status is only known at irregularly spaced and individual-specific assessment  ...  The selection of the tuning parameter λ is an important step in analyses based on penalized likelihood; when λ = ∞, none of the variables will be selected and when λ = 0, all of the variables will be selected  ... 
doi:10.1111/biom.12302 pmid:25773729 fatcat:5i5a44xvl5hulbwnazgw77unxq

SPARSE PREDICTIVE MODELING FOR BANK TELEMARKETING SUCCESS USING SMOOTH-THRESHOLD ESTIMATING EQUATIONS

Yoshinori Kawasaki, Masao Ueki
2015 Journal of the Japanese Society of Computational Statistics  
In addition to variable selection, the smooth-threshold estimating equation can achieve automatic grouping of predictor variables, which is an alternative sparse modeling to perform variable selection  ...  Dealing with a categorical predictor variable as multiple dummy variables increases model dimensionality, and redundancy in model parameterization must be of practical concern.  ...  This study was carried out under the ISM General Cooperative Research 1 (2015-ISM-CRP-1013), and supported by a Grantin-Aid for Young Scientist (B) (25870074) and Grants-in-Aid for Scientific Research  ... 
doi:10.5183/jjscs.1502003_217 fatcat:eidz4v4dnjcgtpshzlf67npx2q

Application of Penalized Regression Techniques in Modelling Insulin Sensitivity by Correlated Metabolic Parameters

Christian S. Göbl, Latife Bozkurt, Andrea Tura, Giovanni Pacini, Alexandra Kautzky-Willer, Martina Mittlböck, Tim Friede
2015 PLoS ONE  
This paper aims to introduce penalized estimation techniques in clinical investigations of diabetes, as well as to assess their possible advantages and limitations.  ...  Data from a previous study was used to carry out the simulations to assess: a) which procedure results in the lowest prediction error of the final model in the setting of a large number of predictor variables  ...  Particular focus was set to assess characteristics of penalized Variable Shrinkage to Model Insulin Sensitivity regression techniques, as compared to commonly used sequential variable selection to introduce  ... 
doi:10.1371/journal.pone.0141524 pmid:26544569 pmcid:PMC4636325 fatcat:7n247kcolnagzeuu7qe2qf7sw4

A shortened version of Raven's standard progressive matrices for children and adolescents

Anna M. Langener, Anne‐Wil Kramer, Wouter den Bos, Hilde M. Huizenga
2021 British Journal of Developmental Psychology  
Therefore, we used a machine learning approach - regularized regression in combination with cross-validation - to develop a short 15-item version.  ...  Numerous developmental studies assess general cognitive ability, not as the primary variable of interest, but rather as a background variable.  ...  Conflicts of interest Data availability statement Data sharing is not applicable to this article as no new data were created in this study.  ... 
doi:10.1111/bjdp.12381 pmid:34041776 fatcat:7koozo2x35aengognrcw6qdlg4

c060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models

Martin Sill, Thomas Hielscher, Natalia Becker, Manuela Zucknick
2014 Journal of Statistical Software  
One is for example often interested in the stability of selected features and in assessing the prediction performance of a model and we provide functions to deal with both of these tasks.  ...  Although we focus on a survival data application of penalized Cox models in this article, the functions in our R package are in general applicable to all types of regression models implemented in the glmnet  ...  We here want to assess the prognostic value of the L 1 -penalized Cox PH regression model fitted in the previous section.  ... 
doi:10.18637/jss.v062.i05 fatcat:n6yumo3uhzgthpfgzth3xwpaey

Analysis of Penalized Regression Methods in a Simple Linear Model on the High-Dimensional Data

Zari Farhadi Zari Farhadi, Reza Arabi Belaghi, Ozlem Gurunlu Alma
2019 American Journal of Theoretical and Applied Statistics  
We used three regulation methods based on penalized regression to select the appropriate model.  ...  Lasso, Ridge and Elastic Net have desirable features; they can simultaneously perform the regulation and selection of appropriate predictor variables and estimate their effects.  ...  Choosing the Tuning Parameter The most important point in selecting penalized regression models is choosing a suitable value for the tuning parameters.  ... 
doi:10.11648/j.ajtas.20190805.14 fatcat:mwht72kzinavxmyuwynh5x4kwy

Split and Conquer Method in Penalized Logistic Regression with Lasso (Application on Credit Scoring Data)

F Shofiyah, A Sofro
2018 Journal of Physics, Conference Series  
The result is there are 5 important independent variables in credit scoring data.  ...  The last variable is how many times debtor has been 60-89 days late pay in the last 2 years.  ...  In addition, credit scoring data requires selecting variables to identify relevant variables. The method that can be used is penalized logistic regression.  ... 
doi:10.1088/1742-6596/1108/1/012107 fatcat:at2ddfztbnfdhezizql554oxfq

Penalized regression and risk prediction in genome-wide association studies

Erin Austin, Wei Pan, Xiaotong Shen
2013 Statistical analysis and data mining  
Penalized regression equipped with variable selection, such as LASSO and SCAD, is deemed to be promising in this setting.  ...  We find that in general penalized regression outperformed unpenalized regression; SCAD, TLP and LASSO performed best for sparse models, while elastic net regression was the winner, followed by ridge, TLP  ...  Next we include all SNPs in each penalized regression model and vary the tuning parameter λ (Figure 9 ).  ... 
doi:10.1002/sam.11183 pmid:24348893 pmcid:PMC3859439 fatcat:kgm7fpbaovbcld43tsatbv24m4

Bootstrap Enhanced Penalized Regression for Variable Selection with Neuroimaging Data

Samantha V. Abram, Nathaniel E. Helwig, Craig A. Moodie, Colin G. DeYoung, Angus W. MacDonald, Niels G. Waller
2016 Frontiers in Neuroscience  
In this paper, we propose a nonparametric bootstrap quantile (QNT) approach for variable selection with neuroimaging data.  ...  Penalized regression yields more stable estimates and sparser models than OLS regression in situations with large numbers of highly correlated neural predictors.  ...  the λ and α tuning parameters (i.e., for assessing how λ and α affect variable selection).  ... 
doi:10.3389/fnins.2016.00344 pmid:27516732 pmcid:PMC4964314 fatcat:naajm5hetngbforkhi34hremiu

Joint Variable Selection of Mean-Covariance Model for Longitudinal Data

Dengke Xu, Zhongzhan Zhang, Liucang Wu
2013 Open Journal of Statistics  
Then, we propose a penalized maximum likelihood method for variable selection in joint mean and covariance models based on this decomposition.  ...  Simulation studies are undertaken to assess the finite sample performance of the proposed variable selection procedure.  ...  Many selection criteria, such as CV, GCV, AIC and BIC selection can be used to select the tuning parameters. Wang et al.  ... 
doi:10.4236/ojs.2013.31004 fatcat:p5t5omugrbgcjdi7ii5rhj4o54

Multilocus association testing with penalized regression

Saonli Basu, Wei Pan, Xiaotong Shen, William S. Oetting
2011 Genetic Epidemiology  
On the other hand, in spite of a rapidly growing body of literature on penalized regression, most focus on variable selection and outcome prediction, for which penalized methods are generally more effective  ...  In multilocus association analysis, since some markers may not be associated with a trait, it seems attractive to use penalized regression with the capability of automatic variable selection.  ...  This confirms the advantage of penalized regression, especially variable selection by Lasso, for parameter estimation and thus outcome prediction.  ... 
doi:10.1002/gepi.20625 pmid:21922539 pmcid:PMC3350336 fatcat:k4v7sknp2naajhf4itkgtcdlgm

Consistent model identification of varying coefficient quantile regression with BIC tuning parameter selection

Qi Zheng, Limin Peng
2016 Communications in Statistics - Theory and Methods  
Furthermore, we formally investigate a BIC-type uniform tuning parameter selector and show that it can ensure consistent model selection.  ...  Under this motivation, we study a general adaptively weighted LASSO penalization strategy in the quantile regression setting, where a continuum of quantile index is considered and coefficients are allowed  ...  tuning parameter selection).  ... 
doi:10.1080/03610926.2015.1010009 pmid:28008212 pmcid:PMC5166990 fatcat:3lophed4unep7dwmnrrrxv5cwu

Dimensionality Reduction of High-Dimensional Highly Correlated Multivariate Grapevine Dataset

Uday Kant Jha, Peter Bajorski, Ernest Fokoue, Justine Vanden Heuvel, Jan van Aardt, Grant Anderson
2017 Open Journal of Statistics  
Data Analysis for wavelength variable selection.  ...  This comparative study of regression methods using a high-dimensional and highly correlated grapevine hyperspectral dataset revealed that the performance of Elastic Net for variable selection yields the  ...  Justine Vanden Heuvel (Cornell University) for her expertise in vineyard physiology, as well as the field teams from Rochester Institute of Technology and Cornell University for their help in collecting  ... 
doi:10.4236/ojs.2017.74049 fatcat:b7kvuzw65zherjiy4nix2h7yva

Application of penalized linear regression methods to the selection of environmental enteropathy biomarkers

Miao Lu, Jianhui Zhou, Caitlin Naylor, Beth D. Kirkpatrick, Rashidul Haque, William A. Petri, Jennie Z. Ma
2017 Biomarker Research  
Conclusions: Penalized linear regression methods are plausible alternatives to traditional variable selection methods, and the suggested methods are applicable to other biomedical studies.  ...  In this study, different variable selection approaches were applied and the performance of these methods was assessed numerically through simulation studies, assuming the correlations among covariates  ...  Acknowledgements The authors wanted to thank all the team member and participants in the PROVIDE study.  ... 
doi:10.1186/s40364-017-0089-4 pmid:28293424 pmcid:PMC5345248 fatcat:nmog2sfhqves3nsug4wuvybiry
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