scholarly journals Bayesian Learning of the Correlation Structure of High-Dimensional Random Objects

2019 ◽  
Author(s):  
Dalia Chakrabarty
Keyword(s):  
Bayesian Learning ◽  
Correlation Structure ◽  
High Dimensional

scholarly journals Understanding the Shift of Correlation Structure in Major Currency Exchange Rate

2014 ◽  
Vol 8 (3) ◽  
Author(s):  
Nur Syahidah Yusoff ◽  
Maman Abdurachman Djauhari
Keyword(s):  
Complex System ◽  
Exchange Rate ◽  
Financial Market ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Correlation Structure ◽  
High Dimensional ◽  
Currency Exchange ◽  
Currency Exchange Rate

In this paper, we study the high dimensional correlation structure of financial market. Correlation structure can be considered as a complex system that relates each variable to the others in terms of correlation. To analyze such complex system, minimum spanning tree is constructed to simplify the network. A case study will be presented and a conclusion will be highlighted.

scholarly journals Combining clinical and molecular data in regression prediction models: insights from a simulation study

2019 ◽  
Vol 21 (6) ◽  
pp. 1904-1919 ◽  
Author(s):  
Riccardo De Bin ◽  
Anne-Laure Boulesteix ◽  
Axel Benner ◽  
Natalia Becker ◽  
Willi Sauerbrei
Keyword(s):  
Prediction Model ◽  
Simulation Study ◽  
Prediction Models ◽  
Molecular Data ◽  
Data Sources ◽  
Correlation Structure ◽  
High Dimensional ◽  
Sources Of Information ◽  
Gene Expressions ◽  
Low Dimensional

Abstract Data integration, i.e. the use of different sources of information for data analysis, is becoming one of the most important topics in modern statistics. Especially in, but not limited to, biomedical applications, a relevant issue is the combination of low-dimensional (e.g. clinical data) and high-dimensional (e.g. molecular data such as gene expressions) data sources in a prediction model. Not only the different characteristics of the data, but also the complex correlation structure within and between the two data sources, pose challenging issues. In this paper, we investigate these issues via simulations, providing some useful insight into strategies to combine low- and high-dimensional data in a regression prediction model. In particular, we focus on the effect of the correlation structure on the results, while accounting for the influence of our specific choices in the design of the simulation study.

Variational Bayesian Learning Theory

2019 ◽  
Author(s):  
Shinichi Nakajima ◽  
Kazuho Watanabe ◽  
Masashi Sugiyama
Keyword(s):  
Learning Theory ◽  
Bayesian Learning ◽  
Variational Bayesian

Blind Identification of Multichannel Systems Based on Sparse Bayesian Learning

2016 ◽  
Vol E99.B (12) ◽  
pp. 2614-2622 ◽  
Author(s):  
Kai ZHANG ◽  
Hongyi YU ◽  
Yunpeng HU ◽  
Zhixiang SHEN ◽  
Siyu TAO
Keyword(s):  
Bayesian Learning ◽  
Blind Identification ◽  
Sparse Bayesian Learning ◽  
Multichannel Systems
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