Forecasting counting and time statistics of compound Cox processes: a focus on intensity phase type process, deletions and simultaneous events

Paula R. Bouzas (), Nuria Ruiz-Fuentes, Carmen Montes-Gijón and Juan Eloy Ruiz-Castro
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Paula R. Bouzas: University of Granada
Nuria Ruiz-Fuentes: University of Jaén
Carmen Montes-Gijón: University of Granada
Juan Eloy Ruiz-Castro: University of Granada

Statistical Papers, 2021, vol. 62, issue 1, No 12, 235-265

Abstract: Abstract Compound Cox processes (CCP) are flexible marked point processes due to the stochastic nature of their intensity. This paper states closed-form expressions of their counting and time statistics in terms of the intensity and of the mean processes. They are forecast by means of principal components prediction models applied to the mean process in order to reach attainable results. A proposition proves that only weak restrictions are needed to estimate the probability of a new occurrence. Additionally, the phase type process is introduced, which important feature is that its marginal distributions are phase type with random parameters. Since any non-negative variable can be approximated by a phase-type distribution, the new stochastic process is proposed to model the intensity process of any point process. The CCP with this type of intensity provides an especially general model. Several simulations and the corresponding study of the estimation errors illustrate the results and their accuracy. Finally, an application to real data is performed; extreme temperatures in the South of Spain are modeled by a CPP and forecast.

Keywords: Compound Cox process; Estimation; Principal components prediction; Phase type process; 60G25; 60G51; 60G55; 62M20; 62M99; 90C15 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00362-019-01092-0

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