Fast Inference for the Latent Space Network Model Using a Case-Control Approximate Likelihood |
| |
Authors: | Adrian E Raftery Xiaoyue Niu Peter D Hoff Ka Yee Yeung |
| |
Institution: | 1. Department of Statistics , University of Washington, Box 354322 , Seattle , WA , 98195-4322;2. Department of Statistics , Penn State University , University Park , PA , 16802;3. Department of Microbiology , University of Washington, Box 358070 , Seattle , WA , 98195-8070 |
| |
Abstract: | Multilevel modeling is a popular statistical technique for analyzing data in hierarchical format, and thus naturally fits within a distributed database framework. We consider the computational aspects of multilevel modeling across distributed databases. In addition, we consider these aspects under a generalization of the multilevel model where the distributed groups (or databases) are allowed to specify different models at both level-1 (individual) and level-2 (group). For a variety of scenarios, we develop the distributed computation algorithm for two-step least squares (LS) estimators and also for iterative MLE estimators of the parameters of interest; in particular, we determine the required data structure at each computing site, the necessary information (original data, cross-product matrices, coefficient vectors), and the order in which such information needs to be passed between sites. Finally, we discuss recursive updating, fault tolerance, and security issues. |
| |
Keywords: | Clustering Genome science Graph Markov chain Monte Carlo Protein–protein interaction Social network |
|
|