Subsampling Bootstrap of Count Features of Networks Public Deposited

http://ir.library.oregonstate.edu/concern/articles/cj82k911w

This is the publisher’s final pdf. The published article is copyrighted by Institute of Mathematical Statistics and can be found at:  http://projecteuclid.org/euclid.aos/1444222079

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  • Analysis of stochastic models of networks is quite important in light of the huge influx of network data in social, information and bio sciences, but a proper statistical analysis of features of different stochastic models of networks is still underway.We propose bootstrap subsampling methods for finding empirical distribution of count features or “moments” (Bickel, Chen and Levina [Ann. Statist. 39 (2011) 2280–2301]) and smooth functions of these features for the networks. Using these methods, we cannot only estimate the variance of count features but also get good estimates of such feature counts, which are usually expensive to compute numerically in large networks. In our paper, we prove theoretical properties of the bootstrap estimates of variance of the count features as well as show their efficacy through simulation. We also use the method on some real network data for estimation of variance and expectation of some count features.
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  • Bhattacharyya, S., & Bickel, P. J. (2015). Subsampling Bootstrap of Count Features of Networks. Annals of Statistics, 43(6), 2384-2411. doi:10.1214/15-AOS1338
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  • description.provenance : Made available in DSpace on 2015-11-19T17:58:08Z (GMT). No. of bitstreams: 2 BhattacharyyaSharmodeepStatisticsSubsamplngBootstrapCount.pdf: 1216318 bytes, checksum: e4a1c86951106658fb09cf8ed84c34f0 (MD5) BhattacharyyaSharmodeepStatisticsSubsamplngBootstrapCountSupplement.pdf: 284438 bytes, checksum: 5ff3410dc10b208a3e6087c8a0db1b85 (MD5) Previous issue date: 2015-12
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