Robust MAVE through nonconvex penalized regression

2021 ◽  
pp. 107247
Author(s):  
Jing Zhang ◽  
Qin Wang ◽  
D'Arcy Mays
Keyword(s):  
Penalized Regression

Penalized Regression Models to Select Biomarkers of Environmental Enteropathy Associated with Linear Growth Acquisition in a Peruvian Birth Cohort

2019 ◽  
Author(s):  
Josh Colston ◽  
Pablo Peñataro Yori ◽  
Lawrence H. Moulton ◽  
Maribel Paredes Olortegui ◽  
Peter S. Kosek ◽  
...  
Keyword(s):  
Birth Cohort ◽  
Regression Models ◽  
Linear Growth ◽  
Penalized Regression ◽  
Environmental Enteropathy

scholarly journals The landscape of gene co-expression modules correlating with prognostic genetic abnormalities in AML

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Chao Guo ◽  
Ya-yue Gao ◽  
Qian-qian Ju ◽  
Chun-xia Zhang ◽  
Ming Gong ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Prediction Model ◽  
Hox Genes ◽  
Expression Profiles ◽  
Penalized Regression ◽  
Diagnostic Utility ◽  
Lasso Regression ◽  
Hub Genes ◽  
Npm1 Mutation ◽  
Genetic Abnormalities

Abstract Background The heterogenous cytogenetic and molecular variations were harbored by AML patients, some of which are related with AML pathogenesis and clinical outcomes. We aimed to uncover the intrinsic expression profiles correlating with prognostic genetic abnormalities by WGCNA. Methods We downloaded the clinical and expression dataset from BeatAML, TCGA and GEO database. Using R (version 4.0.2) and ‘WGCNA’ package, the co-expression modules correlating with the ELN2017 prognostic markers were identified (R2 ≥ 0.4, p < 0.01). ORA detected the enriched pathways for the key co-expression modules. The patients in TCGA cohort were randomly assigned into the training set (50%) and testing set (50%). The LASSO penalized regression analysis was employed to build the prediction model, fitting OS to the expression level of hub genes by ‘glmnet’ package. Then the testing and 2 independent validation sets (GSE12417 and GSE37642) were used to validate the diagnostic utility and accuracy of the model. Results A total of 37 gene co-expression modules and 973 hub genes were identified for the BeatAML cohort. We found that 3 modules were significantly correlated with genetic markers (the ‘lightyellow’ module for NPM1 mutation, the ‘saddlebrown’ module for RUNX1 mutation, the ‘lightgreen’ module for TP53 mutation). ORA revealed that the ‘lightyellow’ module was mainly enriched in DNA-binding transcription factor activity and activation of HOX genes. The ‘saddlebrown’ module was enriched in immune response process. And the ‘lightgreen’ module was predominantly enriched in mitosis cell cycle process. The LASSO- regression analysis identified 6 genes (NFKB2, NEK9, HOXA7, APRC5L, FAM30A and LOC105371592) with non-zero coefficients. The risk score generated from the 6-gene model, was associated with ELN2017 risk stratification, relapsed disease, and prior MDS history. The 5-year AUC for the model was 0.822 and 0.824 in the training and testing sets, respectively. Moreover, the diagnostic utility of the model was robust when it was employed in 2 validation sets (5-year AUC 0.743–0.79). Conclusions We established the co-expression network signature correlated with the ELN2017 recommended prognostic genetic abnormalities in AML. The 6-gene prediction model for AML survival was developed and validated by multiple datasets.

scholarly journals Two‐stage penalized regression screening to detect biomarker–treatment interactions in randomized clinical trials

Biometrics ◽  
2021 ◽  
Author(s):  
Jixiong Wang ◽  
Ashish Patel ◽  
James M.S. Wason ◽  
Paul J. Newcombe
Keyword(s):  
Clinical Trials ◽  
Randomized Clinical Trials ◽  
Penalized Regression ◽  
Two Stage

Multiple structural breaks in cointegrating regressions: a model selection approach

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Alexander Schmidt ◽  
Karsten Schweikert
Keyword(s):  
Structural Breaks ◽  
Penalized Regression ◽  
Adaptive Lasso ◽  
Test Statistics ◽  
New Approach ◽  
Multiple Structural Breaks ◽  
Long Run ◽  
Model Selection Approach ◽  
Regression Techniques ◽  
Lasso Estimator

Abstract In this paper, we propose a new approach to model structural change in cointegrating regressions using penalized regression techniques. First, we consider a setting with known breakpoint candidates and show that a modified adaptive lasso estimator can consistently estimate structural breaks in the intercept and slope coefficient of a cointegrating regression. Second, we extend our approach to a diverging number of breakpoint candidates and provide simulation evidence that timing and magnitude of structural breaks are consistently estimated. Third, we use the adaptive lasso estimation to design new tests for cointegration in the presence of multiple structural breaks, derive the asymptotic distribution of our test statistics and show that the proposed tests have power against the null of no cointegration. Finally, we use our new methodology to study the effects of structural breaks on the long-run PPP relationship.

scholarly journals Improving penalized regression-based clustering model in big data

2021 ◽  
Vol 1897 (1) ◽  
pp. 012036
Author(s):  
Sarah Ghanim Mahmood Al-Kababchee ◽  
Omar Saber Qasim ◽  
Zakariya Yahya Algamal
Keyword(s):  
Big Data ◽  
Penalized Regression ◽  
Clustering Model

scholarly journals Penalized regression via the restricted bridge estimator

2021 ◽  
Author(s):  
Bahadır Yüzbaşı ◽  
Mohammad Arashi ◽  
Fikri Akdeniz
Keyword(s):  
Penalized Regression

scholarly journals The influence function of penalized regression estimators

Statistics ◽  
2014 ◽  
Vol 49 (4) ◽  
pp. 741-765 ◽  
Author(s):  
Viktoria Öllerer ◽  
Christophe Croux ◽  
Andreas Alfons
Keyword(s):  
Influence Function ◽  
Penalized Regression ◽  
Regression Estimators

scholarly journals Survival Analysis by Penalized Regression and Matrix Factorization

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Yeuntyng Lai ◽  
Morihiro Hayashida ◽  
Tatsuya Akutsu
Keyword(s):  
Matrix Factorization ◽  
Regression Models ◽  
Nonnegative Matrix ◽  
B Cell Lymphoma ◽  
Penalized Regression ◽  
Survival Prediction ◽  
Suitable Model ◽  
Gene Expressions ◽  
Combination Methods ◽  
Survival Patterns

Because every disease has its unique survival pattern, it is necessary to find a suitable model to simulate followups. DNA microarray is a useful technique to detect thousands of gene expressions at one time and is usually employed to classify different types of cancer. We propose combination methods of penalized regression models and nonnegative matrix factorization (NMF) for predicting survival. We triedL1- (lasso),L2- (ridge), andL1-L2combined (elastic net) penalized regression for diffuse large B-cell lymphoma (DLBCL) patients' microarray data and found thatL1-L2combined method predicts survival best with the smallest logrankPvalue. Furthermore, 80% of selected genes have been reported to correlate with carcinogenesis or lymphoma. Through NMF we found that DLBCL patients can be divided into 4 groups clearly, and it implies that DLBCL may have 4 subtypes which have a little different survival patterns. Next we excluded some patients who were indicated hard to classify in NMF and executed three penalized regression models again. We found that the performance of survival prediction has been improved with lower logrankPvalues. Therefore, we conclude that after preselection of patients by NMF, penalized regression models can predict DLBCL patients' survival successfully.

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