Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/309571 
Erscheinungsjahr: 
2023
Quellenangabe: 
[Journal:] METRON [ISSN:] 2281-695X [Volume:] 81 [Issue:] 2 [Publisher:] Springer Milan [Place:] Milano [Year:] 2023 [Pages:] 163-180
Verlag: 
Springer Milan, Milano
Zusammenfassung: 
We discuss a bivariate beta distribution that can model arbitrary beta-distributed marginals with a positive correlation. The distribution is constructed from six independent gamma-distributed random variates. While previous work used an approximate and sometimes inaccurate method to compute the distribution's covariance and estimate its parameters, here, we derive all product moments and the exact covariance, which can be computed numerically. Based on this analysis we present an algorithm for estimating the parameters of the distribution using moment matching. We evaluate this inference method in a simulation study and demonstrate its practical use on a data set consisting of predictions from two correlated forecasters. Furthermore, we generalize the bivariate beta distribution to a correlated Dirichlet distribution, for which the proposed parameter estimation method can be used analogously.
Schlagwörter: 
Bivariate beta distribution
Correlated beta distribution
Covariance
Moment matching
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