Advances in Applied Probability

Population models at stochastic times

Enzo Orsingher, Costantino Ricciuti, and Bruno Toaldo

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In this paper we consider time-changed models of population evolution Xf(t) = X(Hf(t)), where X is a counting process and Hf is a subordinator with Laplace exponent f. In the case where X is a pure birth process, we study the form of the distribution, the intertimes between successive jumps, and the condition of explosion (also in the case of killed subordinators). We also investigate the case where X represents a death process (linear or sublinear) and study the extinction probabilities as a function of the initial population size n0. Finally, the subordinated linear birth–death process is considered. Special attention is devoted to the case where birth and death rates coincide; the sojourn times are also analysed.

Article information

Adv. in Appl. Probab., Volume 48, Number 2 (2016), 481-498.

First available in Project Euclid: 9 June 2016

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 60G22: Fractional processes, including fractional Brownian motion
Secondary: 60G55: Point processes

Nonlinear birth process sublinear death process linear death process sojourn time fractional birth process random time


Orsingher, Enzo; Ricciuti, Costantino; Toaldo, Bruno. Population models at stochastic times. Adv. in Appl. Probab. 48 (2016), no. 2, 481--498.

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