Abstract #300277

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JSM 2003 Abstract #300277
Activity Number: 430
Type: Invited
Date/Time: Thursday, August 7, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #300277
Title: Random Effects Models for Network Data
Author(s): Peter Hoff*+
Companies: University of Washington
Address: Dept. of Statistics, Seattle, WA, 98195-4322,
Keywords: social network ; dyadic data ; transitivity ; balance
Abstract:

One impediment to the statistical analysis of network data has been the difficulty in modeling the dependence among the observations. In the very simple case of binary (0-1) network data, some researchers have parameterized network dependence in terms of exponential family representations. Accurate parameter estimation for such models is difficult, and the most commonly used models often display a significant lack of fit. Additionally, such models are generally limited to binary data. In contrast, random effects models have been a widely successful tool in capturing statistical dependence for a variety of data types, and allow for prediction, imputation, and hypothesis testing within a general regression context. We propose novel random effects structures to capture network dependence, which can also provide graphical representations of network structure and variability.

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