Exact tests for singular network data

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...

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Bibliographic Details
Published inAnnals of the Institute of Statistical Mathematics Vol. 67; no. 4; pp. 687 - 706
Main Authors Dinwoodie, Ian H., Pandya, Kruti
Format Journal Article
LanguageEnglish
Published Tokyo Springer Japan 01.08.2015
Springer Nature B.V
Subjects
Algebra
Attraction
Basins
Boolean
Dynamical systems
Economics
Finance
Hypotheses
Importance sampling
Insurance
Integer programming
Management
Mathematical analysis
Mathematical models
Mathematics
Mathematics and Statistics
Methodology
Networks
Polynomials
Statistical analysis
Statistical inference
Statistics
Statistics for Business
Studies
Test methods
Basin of attraction
Sequential importance sampling
Conditional test
Polynomial dynamics
Biological network
Sufficient statistic
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Summary: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.
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ISSN:0020-3157
1572-9052
DOI:10.1007/s10463-014-0472-y