scholarly journals Variable selection for high‐dimensional partly linear additive Cox model with application to Alzheimer's disease

2020 ◽  
Vol 39 (23) ◽  
pp. 3120-3134
Author(s):  
Qiwei Wu ◽  
Hui Zhao ◽  
Liang Zhu ◽  
Jianguo Sun
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Variable Selection ◽  
Cox Model ◽  
High Dimensional ◽  
Selection For

Variable selection for high-dimensional quadratic Cox model with application to Alzheimer’s disease

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Cong Li ◽  
Jianguo Sun
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Variable Selection ◽  
Linear Models ◽  
Cox Model ◽  
Partial Likelihood ◽  
High Dimensional ◽  
Stat Assoc ◽  
Selection For ◽  
Disease Study

AbstractThis paper discusses variable or covariate selection for high-dimensional quadratic Cox model. Although many variable selection methods have been developed for standard Cox model or high-dimensional standard Cox model, most of them cannot be directly applied since they cannot take into account the important and existing hierarchical model structure. For the problem, we present a penalized log partial likelihood-based approach and in particular, generalize the regularization algorithm under marginality principle (RAMP) proposed in Hao et al. (J Am Stat Assoc 2018;113:615–25) under the context of linear models. An extensive simulation study is conducted and suggests that the presented method works well in practical situations. It is then applied to an Alzheimer’s Disease study that motivated this investigation.

scholarly journals Ensembling Variable Selectors by Stability Selection for the Cox Model

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Qing-Yan Yin ◽  
Jun-Li Li ◽  
Chun-Xia Zhang
Keyword(s):  
Variable Selection ◽  
Regression Models ◽  
Cox Model ◽  
High Dimensional ◽  
Linear Regression Models ◽  
Real World Data ◽  
Stability Selection ◽  
False Discovery ◽  
Selection For ◽  
Base Learner

As a pivotal tool to build interpretive models, variable selection plays an increasingly important role in high-dimensional data analysis. In recent years, variable selection ensembles (VSEs) have gained much interest due to their many advantages. Stability selection (Meinshausen and Bühlmann, 2010), a VSE technique based on subsampling in combination with a base algorithm like lasso, is an effective method to control false discovery rate (FDR) and to improve selection accuracy in linear regression models. By adopting lasso as a base learner, we attempt to extend stability selection to handle variable selection problems in a Cox model. According to our experience, it is crucial to set the regularization region Λ in lasso and the parameter λmin properly so that stability selection can work well. To the best of our knowledge, however, there is no literature addressing this problem in an explicit way. Therefore, we first provide a detailed procedure to specify Λ and λmin. Then, some simulated and real-world data with various censoring rates are used to examine how well stability selection performs. It is also compared with several other variable selection approaches. Experimental results demonstrate that it achieves better or competitive performance in comparison with several other popular techniques.

Hippocampal deformities detected in schizophrenia and Alzheimer's disease by high dimensional brain mapping

1999 ◽  
Vol 9 ◽  
pp. 268
Author(s):  
J.G. Csernansky ◽  
L. Wang ◽  
S. Joshi ◽  
M. Gado ◽  
J. Morris ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Mapping ◽  
High Dimensional
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