scholarly journals Optimized application of penalized regression methods to diverse genomic data

2011 ◽  
Vol 27 (24) ◽  
pp. 3399-3406 ◽  
Author(s):  
Levi Waldron ◽  
Melania Pintilie ◽  
Ming-Sound Tsao ◽  
Frances A. Shepherd ◽  
Curtis Huttenhower ◽  
...  
Keyword(s):  
Genomic Data ◽  
Penalized Regression ◽  
Regression Methods

Penalized Regression Methods in the Source Analysis of Face Recognition

2008 ◽  
pp. 621-624
Author(s):  
Mayrim Vega-Hernández ◽  
Eduardo Martínez-Montes ◽  
Jhoanna Pérez-Hidalgo-Gato ◽  
José M. Sánchez-Bornot ◽  
Pedro Valdés-Sosa
Keyword(s):  
Face Recognition ◽  
Penalized Regression ◽  
Source Analysis ◽  
Regression Methods

scholarly journals Performance analysis on least absolute shrinkage selection operator, elastic net and correlation adjusted elastic net regression methods

2015 ◽  
Vol 3 (1) ◽  
pp. 93
Author(s):  
Pascalis Kadaro Matthew ◽  
Abubakar Yahaya
Keyword(s):  
Linear Regression ◽  
Prediction Accuracy ◽  
Penalized Regression ◽  
Ordinary Least Squares ◽  
Complex Model ◽  
Elastic Net ◽  
Data Set ◽  
Regression Methods ◽  
Regression Techniques ◽  
Selection Operator

<p>Some few decades ago, penalized regression techniques for linear regression have been developed specifically to reduce the flaws inherent in the prediction accuracy of the classical ordinary least squares (OLS) regression technique. In this paper, we used a diabetes data set obtained from previous literature to compare three of these well-known techniques, namely: Least Absolute Shrinkage Selection Operator (LASSO), Elastic Net and Correlation Adjusted Elastic Net (CAEN). After thorough analysis, it was observed that CAEN generated a less complex model.</p>

scholarly journals The prediction of live weight of hair goats through penalized regression methods: LASSO and adaptive LASSO

2018 ◽  
Vol 61 (4) ◽  
pp. 451-458
Author(s):  
Suna Akkol
Keyword(s):  
Live Weight ◽  
Information Criterion ◽  
Penalized Regression ◽  
Adaptive Lasso ◽  
Model Complexity ◽  
Coefficient Of Determination ◽  
Explanatory Variables ◽  
Regression Methods ◽  
Bayesian Criterion ◽  
Chest Girth

Abstract. The least absolute selection and shrinkage operator (LASSO) and adaptive LASSO methods have become a popular model in the last decade, especially for data with a multicollinearity problem. This study was conducted to estimate the live weight (LW) of Hair goats from biometric measurements and to select variables in order to reduce the model complexity by using penalized regression methods: LASSO and adaptive LASSO for γ=0.5 and γ=1. The data were obtained from 132 adult goats in Honaz district of Denizli province. Age, gender, forehead width, ear length, head length, chest width, rump height, withers height, back height, chest depth, chest girth, and body length were used as explanatory variables. The adjusted coefficient of determination (Radj2), root mean square error (RMSE), Akaike's information criterion (AIC), Schwarz Bayesian criterion (SBC), and average square error (ASE) were used in order to compare the effectiveness of the methods. It was concluded that adaptive LASSO (γ=1) estimated the LW with the highest accuracy for both male (Radj2=0.9048; RMSE = 3.6250; AIC = 79.2974; SBC = 65.2633; ASE = 7.8843) and female (Radj2=0.7668; RMSE = 4.4069; AIC = 392.5405; SBC = 308.9888; ASE = 18.2193) Hair goats when all the criteria were considered.

scholarly journals Integration Analysis of Three Omics Data Using Penalized Regression Methods: An Application to Bladder Cancer

PLoS Genetics ◽  
2015 ◽  
Vol 11 (12) ◽  
pp. e1005689 ◽  
Author(s):  
Silvia Pineda ◽  
Francisco X. Real ◽  
Manolis Kogevinas ◽  
Alfredo Carrato ◽  
Stephen J. Chanock ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Penalized Regression ◽  
Omics Data ◽  
Regression Methods ◽  
Integration Analysis

scholarly journals Assessment of Weighted Quantile Sum Regression for Modeling Chemical Mixtures and Cancer Risk

2015 ◽  
Vol 14s2 ◽  
pp. CIN.S17295 ◽  
Author(s):  
Jenna Czarnota ◽  
Chris Gennings ◽  
David C. Wheeler
Keyword(s):  
Cancer Risk ◽  
Strong Association ◽  
Body Burden ◽  
Penalized Regression ◽  
Environmental Chemicals ◽  
Adaptive Lasso ◽  
Environmental Chemical ◽  
Site Specific ◽  
Regression Methods ◽  
Correlated Components

In evaluation of cancer risk related to environmental chemical exposures, the effect of many chemicals on disease is ultimately of interest. However, because of potentially strong correlations among chemicals that occur together, traditional regression methods suffer from collinearity effects, including regression coefficient sign reversal and variance inflation. In addition, penalized regression methods designed to remediate collinearity may have limitations in selecting the truly bad actors among many correlated components. The recently proposed method of weighted quantile sum (WQS) regression attempts to overcome these problems by estimating a body burden index, which identifies important chemicals in a mixture of correlated environmental chemicals. Our focus was on assessing through simulation studies the accuracy of WQS regression in detecting subsets of chemicals associated with health outcomes (binary and continuous) in site-specific analyses and in non-site-specific analyses. We also evaluated the performance of the penalized regression methods of lasso, adaptive lasso, and elastic net in correctly classifying chemicals as bad actors or unrelated to the outcome. We based the simulation study on data from the National Cancer Institute Surveillance Epidemiology and End Results Program (NCI-SEER) case-control study of non-Hodgkin lymphoma (NHL) to achieve realistic exposure situations. Our results showed that WQS regression had good sensitivity and specificity across a variety of conditions considered in this study. The shrinkage methods had a tendency to incorrectly identify a large number of components, especially in the case of strong association with the outcome.

scholarly journals Meta-Analysis for Penalized Regression Methods with Multi-Cohort Genome-Wide Association Studies

2016 ◽  
Vol 81 (3) ◽  
pp. 142-149 ◽  
Author(s):  
Chen Lu ◽  
George T. O'Connor ◽  
Josée Dupuis ◽  
Eric D. Kolaczyk
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
Penalized Regression ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Regression Methods ◽  
Genome Wide

scholarly journals High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking

2019 ◽  
Vol 30 (3) ◽  
pp. 697-719 ◽  
Author(s):  
Fan Wang ◽  
Sach Mukherjee ◽  
Sylvia Richardson ◽  
Steven M. Hill
Keyword(s):  
Variable Selection ◽  
Large Scale ◽  
Penalized Regression ◽  
Adaptive Lasso ◽  
High Dimensional ◽  
Finite Sample ◽  
Dantzig Selector ◽  
Regression Methods ◽  
High Dimensional Regression ◽  
Selection And Ranking

AbstractPenalized likelihood approaches are widely used for high-dimensional regression. Although many methods have been proposed and the associated theory is now well developed, the relative efficacy of different approaches in finite-sample settings, as encountered in practice, remains incompletely understood. There is therefore a need for empirical investigations in this area that can offer practical insight and guidance to users. In this paper, we present a large-scale comparison of penalized regression methods. We distinguish between three related goals: prediction, variable selection and variable ranking. Our results span more than 2300 data-generating scenarios, including both synthetic and semisynthetic data (real covariates and simulated responses), allowing us to systematically consider the influence of various factors (sample size, dimensionality, sparsity, signal strength and multicollinearity). We consider several widely used approaches (Lasso, Adaptive Lasso, Elastic Net, Ridge Regression, SCAD, the Dantzig Selector and Stability Selection). We find considerable variation in performance between methods. Our results support a “no panacea” view, with no unambiguous winner across all scenarios or goals, even in this restricted setting where all data align well with the assumptions underlying the methods. The study allows us to make some recommendations as to which approaches may be most (or least) suitable given the goal and some data characteristics. Our empirical results complement existing theory and provide a resource to compare methods across a range of scenarios and metrics.

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