scholarly journals Variable Selection in a Log–Linear Birnbaum–Saunders Regression Model for High-Dimensional Survival Data via the Elastic-Net and Stochastic EM

Technometrics ◽  
2016 ◽  
Vol 58 (3) ◽  
pp. 383-392 ◽  
Author(s):  
Yukun Zhang ◽  
Xuewen Lu ◽  
Anthony F. Desmond
Keyword(s):  
Variable Selection ◽  
Regression Model ◽  
Survival Data ◽  
Elastic Net ◽  
High Dimensional ◽  
Log Linear ◽  
Stochastic Em

scholarly journals Sparse Boosting Based Machine Learning Methods for High-Dimensional Data

2021 ◽  
Author(s):  
Mu Yue
Keyword(s):  
Machine Learning ◽  
Parameter Estimation ◽  
Variable Selection ◽  
Survival Data ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Learning Methods ◽  
Require Time ◽  
Machine Learning Methods ◽  
Boosting Method

In high-dimensional data, penalized regression is often used for variable selection and parameter estimation. However, these methods typically require time-consuming cross-validation methods to select tuning parameters and retain more false positives under high dimensionality. This chapter discusses sparse boosting based machine learning methods in the following high-dimensional problems. First, a sparse boosting method to select important biomarkers is studied for the right censored survival data with high-dimensional biomarkers. Then, a two-step sparse boosting method to carry out the variable selection and the model-based prediction is studied for the high-dimensional longitudinal observations measured repeatedly over time. Finally, a multi-step sparse boosting method to identify patient subgroups that exhibit different treatment effects is studied for the high-dimensional dense longitudinal observations. This chapter intends to solve the problem of how to improve the accuracy and calculation speed of variable selection and parameter estimation in high-dimensional data. It aims to expand the application scope of sparse boosting and develop new methods of high-dimensional survival analysis, longitudinal data analysis, and subgroup analysis, which has great application prospects.

Sure independent screening elastic net for ultra-high dimensional survival data

2021 ◽  
Author(s):  
Ali Hussain AL-Rammahi ◽  
Tahir R. Dikheel
Keyword(s):  
Survival Data ◽  
Elastic Net ◽  
High Dimensional ◽  
Independent Screening

Comparison of variable selection methods for high-dimensional survival data with competing events

2017 ◽  
Vol 91 ◽  
pp. 159-167 ◽  
Author(s):  
Julia Gilhodes ◽  
Christophe Zemmour ◽  
Soufiane Ajana ◽  
Alejandra Martinez ◽  
Jean-Pierre Delord ◽  
...  
Keyword(s):  
Variable Selection ◽  
Survival Data ◽  
High Dimensional ◽  
Selection Methods ◽  
Competing Events

scholarly journals High Dimensional Logistic Regression Model using Adjusted Elastic Net Penalty

2015 ◽  
Vol 11 (4) ◽  
pp. 667 ◽  
Author(s):  
Zakariya Y Algamal ◽  
Muhammad Hisyam Lee
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Elastic Net ◽  
High Dimensional

A group adaptive elastic-net approach for variable selection in high-dimensional linear regression

2017 ◽  
Vol 61 (1) ◽  
pp. 173-188 ◽  
Author(s):  
Jianhua Hu ◽  
Jian Huang ◽  
Feng Qiu
Keyword(s):  
Linear Regression ◽  
Variable Selection ◽  
Elastic Net ◽  
High Dimensional ◽  
Adaptive Elastic Net
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