The Analysis of Hypo-Exponential and Hyper-Exponential Distributions in Solving Performance Measures for General Phase Type Distributions
DOI:
https://doi.org/10.62054/ijdm/0104.13Keywords:
Coxian distribution, Erlang distribution, Hyper-exponential distribution, Hypo-exponential distribution, Phase type distributionAbstract
The importance of phase-type distribution in modeling activities cannot be under emphasized when both a distribution's initial and second moments are accessible, or when the sequence of data points for computing moments is the information available. In continuous time process for an absorbing finite state Markov chain, the phase-type distribution can be thought of as the of the time until absorption, and it is widely used in queueing theories and other fields of applied probabilities. The common phase-type distributions are generalized Erlang, Coxian, Hypo-exponential, and Hyper-exponential distributions. In this study, performance measures of phase-type distribution using Hypo-exponential, and Hyper-exponential distributions have been looked into, in order to provide meaningful study into the probability function, mean, moment, variance, Laplace Stieltjes transform and squared coefficient of variation of phase type distribution. The study started by considering the tractability and memory less properties of exponential distribution, and since these properties are not enough, we examined the journey through a series of exponential phases to arrive at performance measures. Illustrative examples are demonstrated for various cases to arrive at various values for probability functions, Laplace Stieltjes transform, squared coefficient of variation, moment, mean and variance for the phase type distribution.
References
Aalen, O. O. (2014). Phase Type Distribution in Survival Analysis, Encyclopedia of Biostatistics © John Wiley and Sons, https://doi.org/10.1002/9781118445112.stat06048.
Acal, C., Juan E. R., David, M. and Juan, B. (2024). One Cut Point Phase Type Distribution in Reliability. An Application to Resistive Random Variable. Mathematics 9(21),
DOI:10.3390/math9212734.
Agboola, S.O. (2010). The Analysis of Markov Inter-arrival Queues Model with K – Server under Various Service Point. Unpublished M.Sc. (Statistics) thesis submitted to Department of Mathematics, Obafemi Awolowo University Ile – Ife, Nigeria.
Agboola, S.O. (2021). Direct Equation Solving Methods Algorithms Compositions of Lower-Upper Triangular Matrix and Grassman- TaksarHeyman for the Stationary Distribution of Markov Chain.Interantional Journal of Applied Science and Mathematics, 8(6): .87 – 96.
Agboola, S.O. and Ayinde S.A. (2022). On the Application of Succesive Over-relaxation Algorithmic and Block Numerical Iterative Solutions for the Stationary Distribution in Markov Chain. Nigerian Journal of Pure and Applied Sciences, Faculty of Physical and Faculty of Life Sciences, University of Ilorin, Nigeria, 35 (1): 4263 -4272.
Agboola, S. O. and Ayoade, A. A. (2021). Analysis of Reachability Matrix and Absorption Probabilities for Close and Open Classification Group of States in Markov Chain. Nigerian Journal of Scientific Research (NJSR), Faculty of Science, Ahmadu Bello University Zaria, Nigeria, 20 (5): 634 – 639.
AsgerHobolth , Iker Rivas-González, Mogens Bladt and Andreas Futschik (2024). Phase –Type Distributions in Mathematical Population Genetics. Theoretical Population Biology, 157(4). DOI:10.1016/j.tpb.2024.03.001.
Belen, G.O., Cristina, S. and Gregorio, A. (2020). A Phase Type Distribution for the Sum of Two Concatenated Markov Processes Application to Analysis Survival in Bladder Cancer. Mathematics, 8(12), https://doi.org/10.3390/math8122099.
Bo Henry, Lindqvist, A. (2022). Phase Type Models for Competing Risks with Emphasis on Identifiability issues. Springer Nature Link, 29: 318 – 341.
Christian, C. and Stéphane, M. (2010). Phase Type Distributions and Representations. Some Results and Open Problems for System Theory. International Journal of Control, 76(6): 566 – 580.
Cumani, O. (1982). On the Canonical Representation of Homogeneous Markov Processes Modeling Failure Time Distribution.Institute ElettronocoNasionale, Galileo Ferraria Strada Delle Cacce 91 –I- 10135.
Marie, R. (1980). Calculating Equilibrium Probabilities for λ(n)/C_k/1/N Queues. ACM Sigmetrics Performance Evaluation Review, 9(2): 117–125.
Martin, Bladt. (2022). Phase Type Distribution for Claim Severity Regression Modelling. Astin Bulletin, 52(2): 1 – 32.
Neuts, M.F. (1981). Matrix Geometric Solutions in Stochastic Models—An Algorithmic Approach. Johns Hopkins University Press, Baltimore, MD.
Neuts, M.F. (1989). Structured Stochastic Matrices of M/G/1 Type and Their Applications. Marcel Dekker, New York.
O’Cinneide, C. (1989). A continuous multivariate exponential distribution that is multivariate phase type, Statistics and Probability Letters, 7(4): 323 – 325.
Osogami, T. and Harchol-Balter, M. (2002). Necessary and Sufficient Conditions for Representing General Distributions by Coxians. Carnegie Mellon University Technical Report, CMU-CS-02-178. DOI:10.1007/978-3-540-45232-4_12.
Philippe, B. and Sidje, B. (1993). .Transient Solution of Markov Processes by Krylov Subspaces.Technical Report, IRISA—Campus de Beaulieu, Rennes, France.
Ramaswami, V. (1988). A Stable Recursion for the Steady State Vector in Markov chains of M/G/1 type. Commun. Statist. Stochastic Models, 4: 183–188.
Ramaswami, V. and Neuts, M.F. (1980). Some explicit formulas and computational methods for infinite server queues with phase type arrivals, Journal of Applied Probability, 17: 498–514.
Wiliam, S. (2009). Probability,Markov chain, Queueing and Simulation. Princeton University Press, Princeton and Oxford.
Yudong, Wang and Zhi, Scheng. (2023). Phase Type Distributions for Product Return Data with Two Layer Censoring’. Journal of Royal Statistical Society Series C, Applied Statistics, 72(5): 1475 – 1492. DOI:10.1093/jrsssc/qlad079.
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