Parameter estimation for fractional Poisson processes

By Dexter O. Cahoy, Vladimir V.Uchaikin, Wojbor A.Woyczynski. 2010

115 citations recorded by the source corpus.

The paper proposes a formal estimation procedure for parameters of the fractional Poisson process (fPp). Such procedures are needed to make the fPp model usable in applied situations. The basic idea of fPp, motivated by experimental data with long memory is to make the standard Poisson model more flexible by permitting non-exponential, heavy-tailed distributions of interarrival times and different scaling properties. We establish the asymptotic normality of our estimators for the two parameters appearing in our fPp model. This fact permits construction of the corresponding confidence intervals. The properties of the estimators are then tested using simulated data.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/27c72747d33120a1e335ba5a130700da78d4624a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Barnab'as P'oczos

In Finance

1. Enrico Scalas
2. Nikolai N. Leonenko
3. Charles Tadjeran
4. Mark M. Meerschaert

In Statistics and Probability

1. Nikolai N. Leonenko
2. Mailan Trinh
3. Enrico Scalas
4. Barnab'as P'oczos
5. Jeff Schneider

Citations in the corpus

• The fractional non-homogeneous Poisson process (2016, background): “Parameter estimation for the fractional Poisson distribution is studied in Cahoy et al. (2010).”