Loss-based approach to two-piece location-scale distributions with applications to dependent data

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2020-06-01

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Abstract

Two-piece location-scale models are used for modeling data presenting departures from symmetry. In this paper, we propose an objective Bayesian methodology for the tail parameter of two particular distributions of the above family: the skewed exponential power distribution and the skewed generalised logistic distribution. We apply the proposed objective approach to time series models and linear regression models where the error terms follow the distributions object of study. The performance of the proposed approach is illustrated through simulation experiments and real data analysis. The methodology yields improvements in density forecasts, as shown by the analysis we carry out on the electricity prices in Nordpool markets.

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Bayesian inference, Loss-based prior, Objective Bayes, Electricity prices

Citation

Published Version (Please cite this version)

10.1007/s10260-019-00481-x

Publication Info

Leisen, F, L Rossini and C Villa (2020). Loss-based approach to two-piece location-scale distributions with applications to dependent data. Statistical Methods and Applications, 29(2). pp. 309–333. 10.1007/s10260-019-00481-x Retrieved from https://hdl.handle.net/10161/33555.

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Villa

Cristiano Villa

Associate Professor of Statistics at Duke Kunshan University

Prof. Cristiano Villa main research area is in Bayesian statistics, with particular interest in objective methods. His output has been published in several peer-reviewed journals and presented at international conferences, such as the ISBA International Conference, the O-Bayes conference, and the ERCIM conference. In addition to his research, Prof. Villa is deeply committed to teaching and enjoys interacting with students. His teaching interests include probability, statistics, linear modelling, and risk management. Before joining Duke Kunshan University (DKU), Prof. Villa was a member of the Newcastle University (UK) and the University of Kent (UK). Prior to joining academia in 2014, he worked as an auditor and as an advisor for KPMG in several countries, including, Italy, UK, New Zealand, and Singapore. He holds an M.Sc. and a Ph.D. from the University of Kent, UK.


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