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Activity Number: 190
Type: Contributed
Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #316453 View Presentation
Title: Graphical Models by Using a Joint Regression Quantiles Approach
Author(s): Hyonho Chun* and Myung Hee Lee and Ji Hwan Oh
Companies: and Colorado State University and Purdue University
Keywords: Graphical Models ; Conditional Quantiles ; Component Selection ; Generalized Additive Models

A graphical model can be used for describing interrelationships among multiple biological entities such as genes, proteins and metabolites, in which a graph is used to encode conditional independences that are fairly challenging to be inferred without a specific distributional assumption. In many cases, the multivariate Gaussian assumption is made partly for its simplicity but the assumption is violated in many biological datasets. To resolve the problem, we relax the Gaussian assumption by modeling the conditional quantiles flexibly. In fact, the conditional quantiles bear sucient and necessary information to infer the conditional independences under the Gaussian assumption. We demonstrate the advantages of our approach using simulation studies and apply our method to an interesting real biological dataset, where a considerable amount of the dataset violates the Gaussian assumption due to unknown contamination.

Authors who are presenting talks have a * after their name.

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