Discrete multivariate distributions
By O. Vorobyev, Lavrentiy S. Golovkov. 2008
467 citations recorded by the source corpus.
This article brings in two new discrete distributions: multivariate Binomial distribution and multivariate Poisson distribution. Those distributions were created in eventology as more correct generalizations of Binomial and Poisson distributions. Accordingly to eventology new laws take into account full distribution of events. Also, some properties and characteristics of these new multivariate discrete distributions are described.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/ea45a8cc84c2ce54fd416ae3ff0b0085293e5a7b
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In Artificial Intelligence
- Eike C. Brechmann
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- Emiliano A. Valdez
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In Statistics and Probability
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Citations in the corpus
- A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical Networks In Vitro (2008, methodology): “Toassesswhetheracorrelationwassignificant, we first used the hypergeometric distribution (Johnson et al., 1997) to calculate the number of times, M , that spikes from neuron A and neuron B would be expected by chance to occur in the same time bin: where nA and nB…”
- Multivariate random parameters zero-inflated negative binomial regression for analyzing urban midblock crashes (2018, methodology): “, the multivariate Poisson (MVP) model (Johnson et al., 1997; Ma and Kockelman, 2006) and the multivariate negative binomial (MVNB) model (Anastasopoulos et al.”
- Multivariate binomial/multinomial control chart (2014, background): “Now we turn to a general p-way contingency table of size h1 × h2 × . . . × h p (Johnson et al., 1997).”
- Multivariate Negative Binomial Models for Insurance Claim Counts (2014, methodology): “A simple version is the multivariate Poisson model described by Johnson et al. (1997) and Tsionas (2001), where a common covariance term is shared by each pair of count variables. Karlis and Meligkotsidou (2005) proposed an extension by allowing for full covariance”
- Regression Analysis of Multivariate Fractional Data (2016, methodology): “In the present context, a natural choice for the likelihood underlying the application of QML, which generalizes the approach of Papke and Wooldridge (1996) in the univariate case, is provided by the MB p.f. (see Johnson et al., 1997, Ch. 36).”
- Discrete dispersion models and their Tweedie asymptotics (2014, background): “By expanding the FCGF of Zn (µ) we obtain C(t;Zn (µ)) = µ ⊤t+ 1 2 t⊤Σt+O ( n−1/2 ) , which shows that the multivariate Hermite distribution of (cf. Johnson et al. , 1997, p. 274) with mean vector µ and dispersion matrix Σ appears in the limit as n→ ∞.”
- RATES OF CONVERGENCE OF SOME MULTIVARIATE MARKOV CHAINS WITH POLYNOMIAL EIGENFUNCTIONS (2009, background): “Chapter 40 of [32] is dedicated to the properties and history of the Pólya urn model.”
- Flexible dependence modeling of operational risk losses and its impact on total capital requirements (2014, background): “4 Copula modeling of zero losses The random vector W is multivariate binary, for which distributions are however rather nonstandard and often require an excessive number of parameters (see for example Johnson et al. [1997]).”