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Mehdi Omidi

Academic rank: Assistant Professor
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Education: PhD.
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Faculty: Basic Science
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Research

Title
Multivariate Count-distance base Copulas for analysis of Spatial Point Pattern Data
Type
Presentation
Keywords
Spatial point pattern‎, ‎Copula‎, ‎Monte Carlo method‎.
Year
2017
Researchers Mehdi Omidi ، Mohsen Mohammadzadeh

Abstract

‎Copula‎ ‎functions are powerful tools for construction the multivariate‎ ‎distribution of dependent continues variables in terms of their‎ ‎marginal distributions‎. ‎Dependent count discrete data arise in the‎ ‎areas of spatial point pattern process‎. ‎In this fields‎, ‎it is‎ ‎necessary to find the correlation structure of counts variables and‎ ‎the distance to the special focus‎. ‎These dependency is considered‎ ‎based on Poisson-Weibull distribution‎. ‎In this paper‎, ‎we extend the‎ ‎work by \citeauthor{om2016}\citeyearpar{om2016} to trivariate‎ ‎distribution by implementing the introducing of concentric buffers‎ ‎around to the especial focus and pair copula to built a valid‎ ‎spatial point pattern copula‎. ‎Next‎, ‎for prediction of counts‎, ‎trivariate distribution is achieved based on continuous extension of‎ ‎counts random variables‎. ‎Finally‎, ‎based on achieved function we‎ ‎predict the numbers of Rats in terms of the number of Cockroaches‎ ‎and distance to focus‎ ‎in some important regions in Madrid city.