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Your search keyword '"Benedetti, Roberto"' showing total 2 results
Start Over Author "Benedetti, Roberto" Topic distribution (probability theory)
2 results on '"Benedetti, Roberto"'

1. A distance correlation index of spatial dependence for compositional data.

Author

Andreano, M. Simona, Benedetti, Roberto, and Piersimoni, Federica

Subjects
*ENVIRONMENTAL psychology, *DISTRIBUTION (Probability theory)
Abstract

Geographical data in economic, social or environmental sciences are usually recorded as compositions, i.e. relative frequencies, and a common inquiring problem concerns the analysis of these data over different geographical regions. In the present paper we define a new statistical test to verify spatial dependence of such geographical distributions based on distance correlation, a recently introduced measure of dependence between random vectors. The proposed index computes the non‐linear spatial distance between distributions and can be applied on compositional frequency distributions. Simulations and an application on Italian electoral data are presented, to illustrate the capabilities of the proposed test to detect spatial dependence. [ABSTRACT FROM AUTHOR]

Published
2019
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2. Approximate maximum likelihood estimation of the Bingham distribution.

Author

Bee, Marco, Benedetti, Roberto, and Espa, Giuseppe

Subjects
*MAXIMUM likelihood statistics, *DISTRIBUTION (Probability theory), *DENSITY functionals, *SUFFICIENT statistics, *APPROXIMATION theory
Abstract

Maximum likelihood estimation of the Bingham distribution is difficult because the density function contains a normalization constant that cannot be computed in closed form. Given the availability of sufficient statistics, Approximate Maximum Likelihood Estimation (AMLE) is an appealing method that allows one to bypass the evaluation of the likelihood function. The impact of the input parameters of the AMLE algorithm is investigated and some methods for choosing their numerical values are suggested. Moreover, AMLE is compared to the standard approach which numerically maximizes the (approximate) likelihood obtained with the normalization constant estimated via the Holonomic Gradient Method (HGM). For the Bingham distribution on the sphere, simulation experiments and real-data applications produce similar outcomes for both methods. On the other hand, AMLE outperforms HGM when the dimension increases. [ABSTRACT FROM AUTHOR]

Published
2017
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