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Activity Number: 254
Type: Contributed
Date/Time: Monday, August 5, 2013 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #308640
Title: Sufficient Statistic Selection for Dynamic Networks
Author(s): Xizhen Cai*+ and David Hunter
Companies: Pennsylvania State University and Pennsylvania State University
Keywords: variable selection ; Cox proportional hazard model ; penalize partial likelihood
Abstract:

In recent years, counting processes have been applied to model the dynamics and evolution of large network data. Especially for directed interaction networks, people have been using Cox's model as the underlying intensity process, which allows dependency on a set of sufficient statistics (Perry and Wolfe, 2012, Vu et al 2011). These statistics not only involve measures of time-varying network structure, but also some covariates on nodal characteristics. To include a comprehensive set of sufficient statistics may reduce the bias in predicting future interactions, but also give rise to increase of computational complexity. We first consider networks with single-receiver interactions between nodes, and propose to maximize the penalized partial likelihood to select sufficient statistics and estimate coefficients simultaneously. The resulting estimates of coefficients are sparse and enjoy an oracle property. Interaction networks with multi-receivers are also studied. The approximated partial likelihood is used instead to simplify computations that may bring potential bias. We study the theoretical properties of the maximizer resulting from efficient algorithms.


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