A robust elastic net via bootstrap method under sampling uncertainty for significance analysis of high-dimensional design problems

2021 ◽  
pp. 107117
Author(s):  
Hansu Kim ◽  
Tae Hee Lee
Keyword(s):  
Bootstrap Method ◽  
Elastic Net ◽  
High Dimensional ◽  
Sampling Uncertainty ◽  
Design Problems ◽  
Significance Analysis ◽  
Under Sampling

scholarly journals Adaptive Response Surface Method Using Inherited Latin Hypercube Design Points

2003 ◽  
Vol 125 (2) ◽  
pp. 210-220 ◽  
Author(s):  
G. Gary Wang
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Adaptive Response ◽  
High Dimensional ◽  
Latin Hypercube Design ◽  
Latin Hypercube ◽  
Design Problems ◽  
Surface Method ◽  
Adaptive Response Surface Method ◽  
Design Experiments

This paper addresses the difficulty of the previously developed Adaptive Response Surface Method (ARSM) for high-dimensional design problems. ARSM was developed to search for the global design optimum for computation-intensive design problems. This method utilizes Central Composite Design (CCD), which results in an exponentially increasing number of required design experiments. In addition, ARSM generates a complete new set of CCD points in a gradually reduced design space. These two factors greatly undermine the efficiency of ARSM. In this work, Latin Hypercube Design (LHD) is utilized to generate saturated design experiments. Because of the use of LHD, historical design experiments can be inherited in later iterations. As a result, ARSM only requires a limited number of design experiments even for high-dimensional design problems. The improved ARSM is tested using a group of standard test problems and then applied to an engineering design problem. In both testing and design application, significant improvement in the efficiency of ARSM is realized. The improved ARSM demonstrates strong potential to be a practical global optimization tool for computation-intensive design problems. Inheriting LHD points, as a general sampling strategy, can be integrated into other approximation-based design optimization methodologies.

scholarly journals C-mix: A high-dimensional mixture model for censored durations, with applications to genetic data

2018 ◽  
Vol 28 (5) ◽  
pp. 1523-1539
Author(s):  
Simon Bussy ◽  
Agathe Guilloux ◽  
Stéphane Gaïffas ◽  
Anne-Sophie Jannot
Keyword(s):  
Mixture Model ◽  
Proportional Hazards ◽  
Expectation Maximization Algorithm ◽  
Cox Proportional Hazards ◽  
Elastic Net ◽  
Survival Models ◽  
High Dimensional ◽  
Survival Prediction ◽  
Monte Carlo Simulation Study ◽  
Cox Proportional Hazards Models

We introduce a supervised learning mixture model for censored durations (C-mix) to simultaneously detect subgroups of patients with different prognosis and order them based on their risk. Our method is applicable in a high-dimensional setting, i.e. with a large number of biomedical covariates. Indeed, we penalize the negative log-likelihood by the Elastic-Net, which leads to a sparse parameterization of the model and automatically pinpoints the relevant covariates for the survival prediction. Inference is achieved using an efficient Quasi-Newton Expectation Maximization algorithm, for which we provide convergence properties. The statistical performance of the method is examined on an extensive Monte Carlo simulation study and finally illustrated on three publicly available genetic cancer datasets with high-dimensional covariates. We show that our approach outperforms the state-of-the-art survival models in this context, namely both the CURE and Cox proportional hazards models penalized by the Elastic-Net, in terms of C-index, AUC( t) and survival prediction. Thus, we propose a powerful tool for personalized medicine in cancerology.

scholarly journals Inverse finite-size scaling for high-dimensional significance analysis

2018 ◽  
Vol 97 (6) ◽  
Author(s):  
Yingying Xu ◽  
Santeri Puranen ◽  
Jukka Corander ◽  
Yoshiyuki Kabashima
Keyword(s):  
Finite Size ◽  
High Dimensional ◽  
Finite Size Scaling ◽  
Significance Analysis ◽  
Size Scaling

scholarly journals Improved Lasso (ILASSO) for Gene Selection and Classification in High Dimensional DNA Microarray Data

2021 ◽  
Vol 17 (08) ◽  
pp. 91
Author(s):  
Isah Aliyu Kargi ◽  
Norazlina Bint Ismail ◽  
Ismail Bin Mohamad
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
Poor Performance ◽  
Real Data ◽  
Ordinary Least Squares ◽  
Elastic Net ◽  
Likelihood Method ◽  
High Dimensional ◽  
Initial Weight ◽  
Highly Correlated

<p class="0abstract">Classification and selection of gene in high dimensional microarray data has become a challenging problem in molecular biology and genetics. Penalized Adaptive likelihood method has been employed recently for classification of cancer to address both gene selection consistency and estimation of gene coefficients in high dimensional data simultaneously. Many studies from the literature have proposed the use of ordinary least squares (OLS), maximum likelihood estimation (MLE) and Elastic net as the initial weight in the Adaptive elastic net, but in high dimensional microarray data the MLE and OLS are not suitable. Likewise, considering the Elastic net as the initial weight in Adaptive elastic yields a poor performance, because the ridge penalty in the Elastic net grouped coefficient of highly correlated genes closer to each other.  As a result, the estimator fails to differentiate coefficients of highly correlated genes that have different sign being grouped together. To tackle this issue, the present study proposed Improved LASSO (ILASSO) estimator which add the ridge penalty to the original LASSO with an Adaptive weight to both    and  simultaneously. Results from the real data indicated that ILASSO has a better performance compared to other methods in terms of the number of gene selected, classification precision, Sensitivity and Specificity.</p>

Improvement on the Adaptive Response Surface Method for High-Dimensional Computation-Intensive Design Problems

2001 ◽  
Author(s):  
G. Gary Wang
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Adaptive Response ◽  
High Dimensional ◽  
Latin Hypercube ◽  
Design Problems ◽  
Latin Hypercube Designs ◽  
Surface Method ◽  
Adaptive Response Surface Method ◽  
Design Experiments

Abstract This paper addresses the difficulty of the previously developed Adaptive Response Surface Method (ARSM) for high-dimensional design problems. The ARSM was developed to search for the global design optimum for computation-intensive design problems. This method utilized the Central Composite Designs (CCD), which resulted in an exponentially increasing number of required design experiments. In addition, the ARSM generates a complete new set of CCDs in a gradually reduced design space. These two factors greatly undermine the efficiency of the ARSM. In this work, the Latin Hypercube Designs (LHD) were utilized to generate saturated design experiments. Because of the use of Latin Hypercube Designs, the historical design experiments can be inherited in later iterations. The improved ARSM has been tested using a group of standard testing problems and then applied to an engineering design. In both testing and design application, significant efficiency improvement of the ARSM was observed. The ARSM at the current stage demonstrated strong potential to be an efficient global optimization tool for computation-intensive design problems.

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