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Maximal Information Coefficient And Its Application To Brain Network Analysis

Posted on:2014-10-20Degree:MasterType:Thesis
Country:ChinaCandidate:H J JiangFull Text:PDF
GTID:2250330398996907Subject:Applied Mathematics
Abstract/Summary:PDF Full Text Request
With the development of statistical method, the research on its application to all kinds of fields goes deeper and deeper. It plays a key role in many fields, such as Pattern recognition, Magnetic resonance data analysis, time series analysis, Windows error processing, Image process-ing。 Model selection is a key part of statistical methods, and variable selection is one of its crucial problems. This paper proposes a new criterion based on maximal information coefficient(MIC) of model selection and variable selection, and there is an application of MIC to Magnetic resonance data analysis.1. Model selection based on MIC· A procedure of MIC written in C++language is given, which is much more flexible than the one given by David.N.Reshef et al;· A new method in model selection-minimizing the association between residue and explanatory variable is proposed, which is robust to different types of noise, and also an evaluation standard of different model selection criterion;2. Variable selection based on MIC An extension of MIC to Partial MIC is given, which can be used to· Find the inhibited association (Linearity or nonlinearity);· Find the true model;· Make full use of the data;· Find the reason of the dependency;3. Magentic resonance data analysis (Violence tendency in teenagers) We find that· The number of edge within Occipital lobe of patients is less than that of normal ones;· The function of Limibic lobe of patients is weaker than that of normal ones;· The number of edge between Central region and Parietal lobe, Frontal lobe and Lim-ibic lobe, Parietal lobe and Occipital lobe of patients is less than that of normal ones;· The number of edge between Frontal lobe and Temproal lobe of patients is more than that of normal ones;...
Keywords/Search Tags:MIC, dependency, model selection, brain network, violence in teenagers, curvefitting, PMIC
PDF Full Text Request
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