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Exact tests for singular network data

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  • Ian Dinwoodie
  • Kruti Pandya

Abstract

We propose methodology for exact statistical tests of hypotheses for models of network dynamics. The methodology formulates Markovian exponential families, then uses sequential importance sampling to compute expectations within basins of attraction and within level sets of a sufficient statistic for an over-dispersion model. Comparisons of hypotheses can be done conditional on basins of attraction. Examples are presented. Copyright The Institute of Statistical Mathematics, Tokyo 2015

Suggested Citation

  • Ian Dinwoodie & Kruti Pandya, 2015. "Exact tests for singular network data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(4), pages 687-706, August.
    • Handle: RePEc:spr:aistmt:v:67:y:2015:i:4:p:687-706
    DOI: 10.1007/s10463-014-0472-y
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    References listed on IDEAS

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    1. Yuguo Chen & Persi Diaconis & Susan P. Holmes & Jun S. Liu, 2005. "Sequential Monte Carlo Methods for Statistical Analysis of Tables," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 109-120, March.
    2. Scutari, Marco, 2010. "Learning Bayesian Networks with the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i03).
    3. Yuguo Chen & Junyi Xie & Jun S. Liu, 2005. "Stopping‐time resampling for sequential Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 199-217, April.
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