Parameter estimation for fractional Poisson processes

被引:64
作者
Cahoy, Dexter O. [3 ]
Uchaikin, Vladimir V. [4 ]
Woyczynski, Wojbor A. [1 ,2 ]
机构
[1] Case Western Reserve Univ, Dept Stat, Cleveland, OH 44106 USA
[2] Case Western Reserve Univ, Ctr Stochast & Chaot Proc Sci & Technol, Cleveland, OH 44106 USA
[3] Louisiana Tech Univ, Dept Math & Stat, Ruston, LA 71270 USA
[4] Ulyanovsk State Univ, Dept Theoret & Math Phys, Ulyanovsk, Russia
关键词
Fractional Poisson process; Heavy tails; Limit distributions; Confidence intervals; Parameter estimation; Method of moments; INFERENCE;
D O I
10.1016/j.jspi.2010.04.016
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The paper proposes a formal estimation procedure for parameters of the fractional Poisson process (fp(p)). 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. (C) 2010 Elsevier B.V. All rights reserved.
引用
收藏
页码:3106 / 3120
页数:15
相关论文
共 25 条
[1]  
[Anonymous], 2006, THEORY APPL FRACTION
[2]  
[Anonymous], INT J BIFURCATION CH
[3]  
[Anonymous], 1999, Chance and Stability Stable Distributions and their Applications, DOI DOI 10.1515/9783110935974
[4]  
[Anonymous], STABLE NONGAUSSIAN R
[5]   Estimation of Parameters of Fractional Stable Distributions [J].
V. E. Bening ;
V. Yu. Korolev ;
V. N. Kolokol'tsov ;
V. V. Saenko ;
V. V. Uchaikin ;
V. M. Zolotarev .
Journal of Mathematical Sciences, 2004, 123 (1) :3722-3732
[6]  
BOROS G., 2004, IRRESISTIBLE INTEGRA
[7]  
Cahoy D.O, 2007, THESIS CASE W RESERV
[8]  
DiCiccio TJ, 1996, STAT SCI, V11, P189
[9]  
Ferguson T. S., 1996, A Course in Large Sample Theory
[10]  
Haan LD., 1980, J R STAT SOC B, V42, P83