scholarly journals Composite Likelihood Bayesian Information Criteria for Model Selection in High-Dimensional Data

2010 ◽  
Vol 105 (492) ◽  
pp. 1531-1540 ◽  
Author(s):  
Xin Gao ◽  
Peter X.-K. Song
Keyword(s):  
Model Selection ◽  
High Dimensional Data ◽  
Information Criteria ◽  
Composite Likelihood ◽  
High Dimensional

scholarly journals A High-Dimensional Modeling System Based on Analytical Hierarchy Process and Information Criteria

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Tuba Koç
Keyword(s):  
High Dimensional Data ◽  
Information Criteria ◽  
Classification Model ◽  
High Dimensional ◽  
Data Sets ◽  
Suitable Model ◽  
Classification Problems ◽  
Dimensional Modeling ◽  
Novel Approach ◽  
Hierarchy Process

High-dimensional data sets frequently occur in several scientific areas, and special techniques are required to analyze these types of data sets. Especially, it becomes important to apply a suitable model in classification problems. In this study, a novel approach is proposed to estimate a statistical model for high-dimensional data sets. The proposed method uses analytical hierarchical process (AHP) and information criteria for determining the optimal PCs for the classification model. The high-dimensional “colon” and “gravier” datasets were used in evaluation part. Application results demonstrate that the proposed approach can be successfully used for modeling purposes.

scholarly journals Special issue on Model Selection and High Dimensional Data Reduction

2014 ◽  
Vol 71 ◽  
pp. 652-653
Author(s):  
S. Ejaz Ahmed ◽  
Gerda Claeskens ◽  
Hidetoshi Shimodaira ◽  
Stefan Van Aelst
Keyword(s):  
Model Selection ◽  
Data Reduction ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Special Issue
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