Modeling binary familial data using Gaussian copula

2016 ◽  
Vol 46 (20) ◽  
pp. 10097-10102 ◽  
Author(s):  
Yihao Deng
2017 ◽  
Vol 16 ◽  
pp. 95-98 ◽  
Author(s):  
Arsim Kelmendi ◽  
Charilaos Kourogiorgas ◽  
Andrej Hrovat ◽  
Athanasios D. Panagopoulos ◽  
Gorazd Kandus ◽  
...  

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 524
Author(s):  
Walguen Oscar ◽  
Jean Vaillant

Cox processes, also called doubly stochastic Poisson processes, are used for describing phenomena for which overdispersion exists, as well as Poisson properties conditional on environmental effects. In this paper, we consider situations where spatial count data are not available for the whole study area but only for sampling units within identified strata. Moreover, we introduce a model of spatial dependency for environmental effects based on a Gaussian copula and gamma-distributed margins. The strength of dependency between spatial effects is related with the distance between stratum centers. Sampling properties are presented taking into account the spatial random field of covariates. Likelihood and Bayesian inference approaches are proposed to estimate the effect parameters and the covariate link function parameters. These techniques are illustrated using Black Leaf Streak Disease (BLSD) data collected in Martinique island.


2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
T. Mesbahzadeh ◽  
M. M. Miglietta ◽  
M. Mirakbari ◽  
F. Soleimani Sardoo ◽  
M. Abdolhoseini

Precipitation and temperature are very important climatic parameters as their changes may affect life conditions. Therefore, predicting temporal trends of precipitation and temperature is very useful for societal and urban planning. In this research, in order to study the future trends in precipitation and temperature, we have applied scenarios of the fifth assessment report of IPCC. The results suggest that both parameters will be increasing in the studied area (Iran) in future. Since there is interdependence between these two climatic parameters, the independent analysis of the two fields will generate errors in the interpretation of model simulations. Therefore, in this study, copula theory was used for joint modeling of precipitation and temperature under climate change scenarios. By the joint distribution, we can find the structure of interdependence of precipitation and temperature in current and future under climate change conditions, which can assist in the risk assessment of extreme hydrological and meteorological events. Based on the results of goodness of fit test, the Frank copula function was selected for modeling of recorded and constructed data under RCP2.6 scenario and the Gaussian copula function was used for joint modeling of the constructed data under the RCP4.5 and RCP8.5 scenarios.


2016 ◽  
Vol 69 ◽  
pp. 97-103 ◽  
Author(s):  
Edward Furman ◽  
Alexey Kuznetsov ◽  
Jianxi Su ◽  
Ričardas Zitikis

Author(s):  
Arsim Kelmendi ◽  
Charilaos I. Kourogiorgas ◽  
Andrej Hrovat ◽  
Athanasios D. Panagopoulos ◽  
Gorazd Kandus ◽  
...  

2017 ◽  
Vol 59 (3) ◽  
pp. 289-310 ◽  
Author(s):  
Yong He ◽  
Xinsheng Zhang ◽  
Jiadong Ji ◽  
Bin Liu

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