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Activity Number: 191 - Contributed Poster Presentations: Section on Statistical Graphics
Type: Contributed
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Graphics
Abstract #330068
Title: A Joint Modeling Approach for Directed Acyclic Network Data
Author(s): Yan Zhou*
Companies: Merck &Co., Inc
Keywords: Estimating function; Directed acyclic network; Joint modeling; Generalised method of moments; Hybrid quadratic inference function; Shrinkage
Abstract:

This paper concerns regression methodology to assess relationships between multi-dimensional response variables and covariates that are correlated within a network. To address analytic challenges pertaining to the integration of directed acyclic network topology into the regression analysis, we propose a joint regression model for the mean and covariance via the hybrid quadratic inference method that uses both prior and data-driven correlations among network nodes. A Godambe information-based tuning strategy is proposed to allocate weights between the prior and data-driven network structures, so the estimator is efficient. The proposed method is conceptually simple and computationally fast, and has appealing large-sample properties. The effectiveness of the proposed approach is evaluated by simulation and data examples.


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

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