Regression model for surrogate data in high dimensional statistics

2019 ◽  
Vol 49 (13) ◽  
pp. 3206-3227 ◽  
Author(s):  
Firas Ibrahim ◽  
Ali Hajj Hassan ◽  
Jacques Demongeot ◽  
Mustapha Rachdi
Keyword(s):  
Regression Model ◽  
Surrogate Data ◽  
High Dimensional ◽  
High Dimensional Statistics

scholarly journals SPReM: Sparse Projection Regression Model For High-Dimensional Linear Regression

2015 ◽  
Vol 110 (509) ◽  
pp. 289-302 ◽  
Author(s):  
Qiang Sun ◽  
Hongtu Zhu ◽  
Yufeng Liu ◽  
Joseph G. Ibrahim
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
High Dimensional ◽  
Sparse Projection

scholarly journals Cancer classification and biomarker selection via a penalized logsum network-based logistic regression model

2021 ◽  
Vol 29 ◽  
pp. 287-295
Author(s):  
Zhiming Zhou ◽  
Haihui Huang ◽  
Yong Liang
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Gene Selection ◽  
Simulated Data ◽  
Biological Data ◽  
Cancer Classification ◽  
High Dimensional ◽  
Data Set ◽  
Biomarker Selection

BACKGROUND: In genome research, it is particularly important to identify molecular biomarkers or signaling pathways related to phenotypes. Logistic regression model is a powerful discrimination method that can offer a clear statistical explanation and obtain the classification probability of classification label information. However, it is unable to fulfill biomarker selection. OBJECTIVE: The aim of this paper is to give the model efficient gene selection capability. METHODS: In this paper, we propose a new penalized logsum network-based regularization logistic regression model for gene selection and cancer classification. RESULTS: Experimental results on simulated data sets show that our method is effective in the analysis of high-dimensional data. For a large data set, the proposed method has achieved 89.66% (training) and 90.02% (testing) AUC performances, which are, on average, 5.17% (training) and 4.49% (testing) better than mainstream methods. CONCLUSIONS: The proposed method can be considered a promising tool for gene selection and cancer classification of high-dimensional biological data.

ChristopheGiraud. Introduction to High‐Dimensional Statistics. Boca Raton, CRC Press.

Biometrics ◽  
2018 ◽  
Vol 74 (4) ◽  
pp. 1524-1525
Author(s):  
Katharina Parry ◽  
Matthieu Vignes
Keyword(s):  
High Dimensional ◽  
High Dimensional Statistics

scholarly journals Estimating and testing the microbial causal mediation effect with high-dimensional and compositional microbiome data

2019 ◽  
Author(s):  
Chan Wang ◽  
Jiyuan Hu ◽  
Martin J Blaser ◽  
Huilin Li
Keyword(s):  
Regression Model ◽  
Association Studies ◽  
Statistical Significance ◽  
Mediation Effect ◽  
Supplementary Information ◽  
High Dimensional ◽  
Model Framework ◽  
Mediation Effects ◽  
Causal Mediation ◽  
Microbiome Data

Abstract Motivation Recent microbiome association studies have revealed important associations between microbiome and disease/health status. Such findings encourage scientists to dive deeper to uncover the causal role of microbiome in the underlying biological mechanism, and have led to applying statistical models to quantify causal microbiome effects and to identify the specific microbial agents. However, there are no existing causal mediation methods specifically designed to handle high dimensional and compositional microbiome data. Results We propose a rigorous Sparse Microbial Causal Mediation Model (SparseMCMM) specifically designed for the high dimensional and compositional microbiome data in a typical three-factor (treatment, microbiome and outcome) causal study design. In particular, linear log-contrast regression model and Dirichlet regression model are proposed to estimate the causal direct effect of treatment and the causal mediation effects of microbiome at both the community and individual taxon levels. Regularization techniques are used to perform the variable selection in the proposed model framework to identify signature causal microbes. Two hypothesis tests on the overall mediation effect are proposed and their statistical significance is estimated by permutation procedures. Extensive simulated scenarios show that SparseMCMM has excellent performance in estimation and hypothesis testing. Finally, we showcase the utility of the proposed SparseMCMM method in a study which the murine microbiome has been manipulated by providing a clear and sensible causal path among antibiotic treatment, microbiome composition and mouse weight. Availability and implementation https://sites.google.com/site/huilinli09/software and https://github.com/chanw0/SparseMCMM. Supplementary information Supplementary data are available at Bioinformatics online.

Sign in / Sign up

Export Citation Format

Share Document