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清華大學統計學研究所
國立清華大學統計學研究所
2016-12-08(四),主講人:Prof. George Michailidis (University of Florida)

清華大學、交通大學

統 計 學 研 究 所

專 題 演 講

 


講 題: Estimating High-dimensional Multi-layered Networks Through Penalized Maximum Likelihood
演講者:

Prof. George Michailidis

Director of Informatics Institute

Professor of Statistics, Computer and Information Sciences, University of Florida

時 間: 105年12月08日(星期五)13:40 - 15:00  (15:00 - 15:20 茶會於統計所821室舉行)
地 點: 綜合三館837室
摘 要: Gaussian graphical models represent a good tool for capturing interactions between nodes represent the underlying random variables. However, in many applications in biology one is interested in modeling associations both between, as well as within molecular compartments (e.g., interactions between genes and proteins/metabolites). To this end, inferring multi-layered network structures from high-dimensional data provides insight into understanding the conditional relationships among nodes within layers, after adjusting for and quantifying the effects of nodes from other layers. We propose an integrated algorithmic approach for estimating multi-layered networks, that incorporates a screening step for significant variables, an optimization algorithm for estimating the key model parameters and a stability selection step for selecting the most stable effects. The proposed methodology offers an efficient way of estimating the edges within and across layers iteratively, by solving an optimization problem constructed based on penalized maximum likelihood (under a Gaussianity assumption). The optimization is solved on a reduced parameter space that is identified through screening, which remedies the instability in high-dimension. Theoretical properties are considered to ensure identifiability and consistent estimation of the parameters and convergence of the optimization algorithm, despite the lack of global convexity. The performance of the methodology is illustrated on synthetic data sets and on an application on gene and metabolic expression data for patients with renal disease.

 

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