ASYMPTOTIC BEHAVIOR OF THE KOLMOGOROV-SMIRNOV TEST FOR POISSON PROCESSES WITH SCALE PARAMETER
Keywords:
inhomogeneous Poisson process, parametric basic hypothesis, Kolmogorov-Smirnov Mises test, asymptotically parameter free testDOI:
https://doi.org/10.17654/0972361724053Abstract
We studied the Kolmogorov-Smirnov test for inhomogeneous Poisson processes with parametric null hypothesis. The unknown parameter is a scale parameter. The construction of the test is based on the MLE of this parameter. The main result shows that due to the structure of the statistic, the substitution of the estimator in the place of the unknown parameter leads to the limit of the statistic test with distribution which does not depend on the unknown parameter.
Received: March 20, 2024
Revised: May 15, 2024
Accepted: June 13, 2024
References
P. Billingsley, Convergence of Probability Measure, J. Wiley, New York, 1999.
A. S. Dabye, Estimation paramétrique pour un processus de Poisson d’intensité discontinue, Thèse de doctorat de l’Université du Maine, 1999.
A. S. Dabye, On the Cramer-von Mises test with parametric hypothesis for Poisson processes, Stat. Inference Stoch. Process. 16(1) (2013), 1-13.
A. S. Dabye, A. Diakhaby and F. N. Diop, On the goodness-of-fit tests for Poisson processes with a discontinuous function intensity, Far East Journal of Theoretical Statistics 37(1) (2011), 23-43.
A. S. Dabye, Yu. A. Kutoyants and E. D. Tanguep, On APF test for Poisson process with shift and scale parameters, Statist. Probab. Lett. 145 (2019), 28-36.
A. S. Dabye, D. W. Tanguep and A. Top, On the Cramer-von Mises test for Poisson process with scale parameter, Far East Journal of Theoretical Statistics 52(6) (2016), 419-441.
S. Dachian and Yu. A. Kutoyants, Hypotheses testing: Poisson versus self-exciting, Scand. J. Statist. 33 (2006), 391-408.
S. Dachian and Yu. A. Kutoyants, On the goodness-of-fit testing for some continuous time processes, Statistical Models and Methods for Biomedical and Technical Systems, F. Vonta et al., eds., Birkhuser, Boston, 2007, pp. 395-413.
R. B. D’Agostino and M. A. Stephens, Goodness of Fit Techniques, Marcel Dekker, New York, 1986.
D. J. Daley and D. Vere-Jones, An Introduction to the Theory of Point Processes, Springer, New York, 1988.
D. A. Darling, The Kolmogorov-Smirnov, Cramer-von Mises tests, Annals of Mathematical Statistics 28 (1957), 823-838.
D. A. Darling, The Cramer-Smirnov test in the parametric case, Annals of Mathematical Statistics 26 (1958), 1-20.
R. Davies, Testing the hypothesis that a point process is Poisson, Adv. Appl. Probab. 9 (1977), 724-746.
F. N. Diop, Tests d’Ajustement d’Hypothèses Composites pour les Processus de Poisson non Homogènes, Thèse de doctorat, Université Gaston Berger de Saint-Louis, Senegal, 2010.
J. Durbin, Distribution Theory for Tests Based on the Sample Distribution Function, SIAM, Philadelphia, 1973.
I. I. Gikhman and A. V. Skorohod, The Theory of Stochastic Processes I, Springer-Verlag, New York, 1974.
P. E. Greenwood and M. Nikulin, A Guide of Chi-squared Testing, John Wiley and Sons, New York, 1996.
Yu. I. Ingster, Asymptotically minimax hypothesis testing for nonparametric alternatives. I, II, III, Math. Methods Statist. 2 (1993), 85-114, 171-189, 249-268.
Yu. I. Ingster and Yu. A. Kutoyants, Nonparametric hypothesis testing for intensity of the Poisson process, Math. Methods Statist. 16(3) (2007), 217-245.
Yu. I. Ingster and I. A. Suslina, Nonparametric Goodness-of-Fit Testing Under Gaussian Models, Springer-Verlag, New York, 2003.
E. Khmaladze, Martingale approach in the theory of goodness-of-fit tests, Theory Probab. Appl. 26(2) (1981), 240-257.
I. A. Ibragimov and R. Z. Khasminskii, Statistical Estimation, Asymptotic Theory, Springer, New York, 1981.
Yu. A. Kutoyants, Parameter Estimation for Stochastic Processes, Heldermann- Verlag, Berlin, 1984.
Yu. A. Kutoyants, Statistical inference for spatial Poisson processes, Lecture Notes in Statistics, 134, Springer-Verlag, New York, 1998.
Yu. A. Kutoyants, Introduction to the Statistics of Poisson Processes and Applications, Springer, Cham, 2023.
E. L. Lehmann and J. P. Romano, Testing Statistical Hypothesis, 3rd ed., Springer-Verlag, New York, 2005.
G. Martynov, Note on the Cramer-von Mises test with estimated parameters, Publ. Math. Debrecen 76(3) (2010), 341-346.
I. Negri and L. Zhou, On goodness-of-fit testing for ergodic diffusion process with shift parameter, Stat. Inference Stoch. Process. 11(1) (2014), 51-73.
C. R. Rao, Linear Statistical Inference and its Applications, Wiley, New York, 1965.
D. Revuz and M. Yor, Continuous martingales and Brownian motion, Vol. 293, 3rd ed., Grundlehren der mathematischen Wissenschaften, Springer, Berlin, 1999.
A. Van der Vaart and J. Wellner, Weak Convergence and Empirical Processes, Springer-Verlag, New York, USA, 1996.
L. Weiss, Testing fit with nuisance location and scale parameters, Naval Research Logistics Quarterly 22(1) (1975), 55-63.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Pushpa Publishing House, Prayagraj, India

This work is licensed under a Creative Commons Attribution 4.0 International License.
____________________________
Attribution: Credit Pushpa Publishing House as the original publisher, including title and author(s) if applicable.
No Derivatives: Modifying or creating derivative works not allowed without written permission.
Contact Pushpa Publishing House for more info or permissions.
Journal Impact Factor: 