PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Distributional properties of the negative binomial Lévy process

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The geometric distribution leads to a Lévy process parameterized by the probability of success. The resulting negative binomial process (NBP) is a purely jump and non-decreasing process with general negative binomial marginal distributions. We review various stochastic mechanisms leading to this process, and study its distributional structure. These results enable us to establish strong convergence of the NBP in the supremum norm to the gamma process, and lead to a straightforward algorithm for simulating sample paths. We also include a brief discussion of estimation of the NPB parameters, and present an example from hydrology illustrating possible applications of this model.
Rocznik
Strony
43--71
Opis fizyczny
Bibliogr. 108 poz., wykr.
Twórcy
  • University of Nevada, Department of Mathematics and Statistics, University of Nevada, Reno, NV 89557, USA
  • Lund University, Centre for Mathematical Sciences, Mathematical Statistics, Lund University, Box 118, 221 00 Lund, Sweden
Bibliografia
  • [1] O. O. Aalen, Modelling heterogeneity in survival analysis by the compound Poisson distribution, Ann. Appl. Probab. 2 (1992), pp. 951-972.
  • [2] F. J. Anscombe, Sampling theory of the negative binomial and logarithmic series distributions, Biometrika 37 (3/4) (1950), pp. 358-382.
  • [3] A. G. Arbous and J. E. Kerrich, Accident statistics and the concept of accident proneness, Biometrics 7 (1951), pp. 340-432.
  • [4] N. Arley, On the Theory of Stochastic Processes and Their Application to the Theory of Cosmic Radiation, Copenhagen 1943.
  • [5] S. K. Bar-Lev and P. Enis, Reproducibility and natural exponential families with power variance functions, Ann. Statist. 14 (1986), pp. 1507-1522.
  • [6] O. Barndorff-Nielsen and G. F. Yeo, Negative binomial process, J. Appl. Probab. 6 (1969), pp. 633-647.
  • [7] J. J. Bartko, The negative binomial distribution: A review of properties and applications, The Virginia Journal of Science 12 (1961), pp. 18-37.
  • [8] J. Betancort-Rijo, Generalized negative binomial distributions, J. Statist. Phys. 98 (2000), pp. 917-933.
  • [9] C. I. Bliss and R. A. Fisher, Fitting the negative binomial distribution to biological data and note on the efficient fitting of the negative binomial, Biometrics 9 (1953), pp. 176-200.
  • [10] L. Bondesson, On simulation from infinitely divisible distributions, Adv. in Appl. Probab. 14 (1982), pp. 855-869.
  • [11] M. T. Boswell and G. P. Patil, Chance mechanisms generating the negative binomial distributions, in: Random Counts in Scientific Work, 1: Random Counts in Models and Structures, G. P. Patil (Ed.), Pennsylvania State University Press, University Park, 1970, pp. 3-22.
  • [12] A. Brix, Generalized gamma measures and shot-noise Cox processes, Adv. in Appl. Probab. 31 (4) (1999), pp. 929-953.
  • [13] A. Brix and W. S. Kendall, Simulation of cluster point processes without edge effects, Adv. in Appl. Probab. 34 (2002), pp. 267-280.
  • [14] F. T. Bruss and T. S. Ferguson, High-risk and competitive investment models, Ann. Appl. Probab. 12 (4) (2002), pp. 1202-1226.
  • [15] F. T. Bruss and L. C. G. Rogers, Pascal processes and their characterization, Stochastic Process. Appl. 37 (2) (1991), pp. 331-338.
  • [16] R. T. Burnett and M. T. Wasan, The negative binomial point process and its inference, in: Multivariate Statistical Analysis, R. P. Gupta (Ed.), North-Holland Publishing Co., Amsterdam-New York 1980, pp. 31-45.
  • [17] Q. L. Burrell, A note on ageing in a library circulation model, Journal of Documentation 41 (1985), pp. 100-115.
  • [18] Q. L. Burrell, Stochastic modelling of the first-citation distribution, Scientometrics 52 (2001), pp. 3-12.
  • [19] Q. L. Burrell, On the nth-citation distribution and obsolescence, Scientometrics 53 (2002), pp. 309-323.
  • [20] Q. L. Burrell, Predicting future citation behavior, Journal of the American Society for Information Science and Technology 54 (5) (2003), pp. 372-378.
  • [21] Q. L. Burrell and V. R. Cane, The analysis of library data, J. Roy. Statist. Soc. Ser. A 145 (1982), pp. 439-471.
  • [22] J. Burridge, Empirical Bayes analysis of survival time data, J. Roy. Statist. Soc. Ser. B 43 (1) (1981), pp. 65-75.
  • [23] V. R. Cane, The concept of accident proneness, Bull. Inst. Math. Bulgaria 15 (1972), pp. 183-189.
  • [24] V. R. Cane, A class of non-identifiable stochastic models, J. Appl. Probab. 14 (1976), pp. 475-482.
  • [25] P. Carruthers and D.-V. Minh, A connection between galaxy probabilities in Zwicky clusters counting distributions in particle physics and quantum optics, Phys. Lett. 131B (1, 2, 3) (1983), pp. 116-120.
  • [26] P. Carruthers and C. C. Shih, Correlations and fluctuations in hadronic multiplicity distributions: the meaning of KNO scaling, Phys. Lett. 127B (3-4) (1983), pp. 242-250.
  • [27] C. Chatfield, A marketing application of a characterization theorem, in: Statistical Distributions in Scientific Work, 2: Model Building and Model Selection, G. P. Patil, S. Kotz and J. K. Ord (Eds.), Reidel, Dordrecht 1975, pp. 175-185.
  • [28] C. Chatfield, A. S. C. Ehrenberg and G. J. Goodhardt, Progress on a simplified model of stationary purchasing behaviour (with discussions), J. Roy. Statist. Soc. Ser. A 129 (1966), pp. 317-367.
  • [29] S. Chaturvedi, V. Gupta and S. K. Soni, A unified view of multiplicity distributions based on a pure birth process, Modern Phys. Lett. A 9 (36) (1994), pp. 3359-3366.
  • [30] S. J. Clark and J. N. Perry, Estimation of the negative binomial parameter k by maximum quasi-likelihood, Biometrics 45 (1989), pp. 309-316.
  • [31] A. D. Cliff and J. K. Ord, Spatial Processes: Models and Applications, Pion Ltd., London 1981.
  • [32] P. Clifford and G. Wei, The equivalence of the Cox process with squared radial Ornstein-Uhlenbeck intensity and the death process in a sample population model, Ann. Appl. Probab. 3 (3) (1993), pp. 863-873.
  • [33] R. Cont and P. Tankov, Financial Modelling with Jump Processes, Chapman and Hall / CRC, Boca Raton 2004.
  • [34] S. Dahiya and M. Kaur, Multifractal multiplicity distribution in e+e− and pp interactions from nonlinear Markov process, J. Phys. G: Nucl. Part. Phys. 24 (1998), pp. 2027-2035.
  • [35] W. Dershowitz, G. Lee, J. Geier, T. Foxford, P. La Pointe and A. Thomas, User documentation, FracMan, Interactive discrete data analysis, geometric modeling, and exploration simulation, Version 2.6 Golder Associates Inc., Seattle 1998.
  • [36] L. Devroye, Non-Uniform Random Variate Generation, Springer, New York 1986.
  • [37] P. J. Diggle and R. K. Milne, Negative binomial counts and point processes, Scand. J. Statist. 10 (1983), pp. 257-267.
  • [38] J. B. Douglas, Analysis with Standard Contagious Distributions, Internat. Coop. Publishing House, Burtonsville, MD, 1980.
  • [39] A. S. C. Ehrenberg, The pattern of consumer purchases, Applied Statistics 8 (1) (1959), pp. 26-41.
  • [40] O. Eneroth, On the quantity of seed in patch sowing, and the connection between the number of seedlings and the stand density in natural regeneration (in Swedish), Norrlands Skogsvardsförbunds Tidskrift 1945, pp. 161-222.
  • [41] D. A. Evans, Experimental evidence concerning contagious distributions in ecology, Biometrika 40 (1953), pp. 186-211.
  • [42] W. Feller, Die Grundlagen der Volterraschen Theorie, Acta Biotheoretica 5 (1939), pp. 11-40.
  • [43] W. Feller, An Introduction to Probability Theory and Its Applications, Vol. 1, Wiley, New York 1960.
  • [44] W. Feller, Introduction to the Theory of Probability and Its Applications, Vol. 2, 2nd edition, Wiley, New York 1971.
  • [45] T. S. Ferguson and M. J. Klass, A representation of independent increment processes without Gaussian components, Ann. Math. Statist. 43 (1972), pp. 1634-1643.
  • [46] C. Ferreri, On the extended Pólya process and some its interpretations, Metron 41 (1-2) (1983), pp. 11-27.
  • [47] C. Ferreri, On a hyperbinomial process, Comm. Statist. Theory Methods 25 (1996), pp. 83-103.
  • [48] R. A. Fisher, The negative binomial distribution, Ann. Eugen 11 (1941-2), pp. 182-187.
  • [49] W. H. Furry, On fluctuation phenomena in the passage of high energy electrons through lead, Phys. Rev. 52 (1937), pp. 569-581.
  • [50] P. A. Gillespie, C. B. Howard, J. J. Wash and J. Watterson, Measurement and characterization of spatial distributions of fractures, Tectonophysics 226 (1993), pp. 113-141.
  • [51] W. Glänzel and A. Schubert, Predictive aspects of a stochastic model for citation processes, Information Processing and Management 31 (1995), pp. 69-80.
  • [52] G. J. Goodhardt, A. S. C. Ehrenberg and C. Chatfield, The Dirichlet: A comprehensive model of buying behaviour (with discussions), J. Roy. Statist. Soc. Ser. A 147 (1984), pp. 621-655.
  • [53] J. Grandell, Doubly Stochastic Poisson Processes, Springer, New York 1976.
  • [54] M. Greenwood and G. U. Yule, An inquiry into the nature of frequency distributions representative of multiple happenings with particular reference to the occurrence of multiple attacks of disease or of repeated accidents, J. Roy. Statist. Soc. Ser. A 83 (1920), pp. 255-279.
  • [55] G. Gregoire, Negative binomial distributions for point processes, Stochastic Process. Appl. 16 (1983), pp. 179-188.
  • [56] V. K. Gupta and E. C. Waymire, Multiscaling properties of spatial rainfall in river flow distributions, J. Geophys. Res. 95 (1990), pp. 1999-2009.
  • [57] G. Gustafson and A. Frans son, The use of the Pareto distribution for fracture transmissivity assessment, Hydrogeology J. 14 (2005), pp. 15-20.
  • [58] J. B. S. Haldane, On a method of estimating frequencies, Biometrika 33 (1945), pp. 222-225.
  • [59] P. Hougaard, Life table methods for heterogeneous populations: distributions describing the heterogeneity, Biometrika 71 (1) (1984), pp. 75-83.
  • [60] P. Hougaard, Survival models for heterogeneous populations derived from stable distributions, Biometrika 73 (1986), pp. 387-397 (correction: 75, p. 395).
  • [61] P. Hougaard, Modelling multivariate survival, Scand. J. Statist. 14 (4) (1987), pp. 291-304.
  • [62] P. Hougaard, M.-L. T. Lee and G. A. Whitmore, Analysis of overdispersed count data by mixtures of Poisson variables and Poisson processes, Biometrics 53 (1997), pp. 1225-1238.
  • [63] J. O. Irwin, Discussion on Chambers and Yule’s paper, J. Roy. Statist. Soc. Ser. B 7 (1941), pp. 101-109.
  • [64] K. G. Janardan and D. J. Schaeffer, Application of discrete distributions for estimating the number of organic compounds in water, in: Statistical Distributions in Scientific Work 6: Applications in Physical, Social, and Life Sciences, C. Taillie, G. P. Patil, and B. A. Baldessari (Eds.), Reidel, Dordrecht 1981, pp. 79-94.
  • [65] N. L. Johnson, S. Kotz and A. W. Kemp, Univariate Discrete Distributions, Wiley, New York 1993.
  • [66] B. Jorgensen, Exponential dispersion models, J. Roy. Statist. Soc. Ser. B 49 (1987), pp. 127-162.
  • [67] Y. Y. Kagan and D. D. Jackson, Probabilistic forecasting of earthquakes, Geophys. J. Int. 143 (2000), pp. 438-453.
  • [68] J. D. Kalbfleisch, Bayesian analysis of survival distributions, J. Roy. Statist. Soc. Ser. B 40 (1978), pp. 214-221.
  • [69] D. E. Kashcheev, Compound Cox processes and option pricing, J. Math. Sci. 106 (2001), pp. 2682-2690.
  • [70] D. G. Kendall, Stochastic processes and population growth, J. Roy. Statist. Soc. Ser. B 11 (2) (1949), pp. 230-282.
  • [71] T. J. Kozubowski, M. M. Meerschaert and G. Gustafson, A new stochastic model for fracture transmissivity assessment, Water Resources Res. 44 (2008), W02435, doi:10.1029/2007WR006053.
  • [72] T. J. Kozubowski, M. M. Meerschaert and K. Podgórski, Fractional Laplace motion, Adv. in Appl. Probab. 38 (2006), pp. 451-464.
  • [73] T. J. Kozubowski and K. Podgórski, Invariance properties of the negative binomial Lévy process and stochastic self-similarity, Intern. Math. Forum 2 (29-32) (2007), pp. 1457-1468.
  • [74] D. G. Lampard, A stochastic process whose successive intervals between events from a first order Markov chain. I, J. Appl. Probab. 5 (1968), pp. 648-668.
  • [75] M.-L. T. Lee and G. A. Whitmore, Stochastic processes directed by randomized time, J. Appl. Probab. 30 (1993), pp. 302-314.
  • [76] R. Lüders, Die Statistik der seltenen Ereignisse, Biometrika 26 (1934), pp. 108-128.
  • [77] J. S. Martin and S. K. Katti, Fitting of some contagious distributions to some available data by the maximum likelihood method, Biometrics 21 (1965), pp. 34-48.
  • [78] B. Matérn, Doubly stochastic Poisson processes in the plane (with discussions), Statistical Ecology 1 (1971), pp. 195-213.
  • [79] A. G. McKendrick, Studies on the theory of continuous probabilities with special reference to its bearing on natural phenomena of a progressive nature, Proc. London Math. Soc. 2 (13) (1914), pp. 401-416.
  • [80] A. G. McKendrick, Applications of mathematics to medical problems, Proc. Edinb. Math. Soc. 44 (1925), pp. 1-34.
  • [81] E. McKenzie, Autoregressive moving-average processes with negative-binomial and geometric marginal distributions, Adv. in Appl. Probab. 18 (1986), pp. 679-705.
  • [82] A. Milne, The comparison of sheep-tick populations (Ixodes ricinus L.), Ann. Appl. Biol. 30 (1943), pp. 240-250.
  • [83] J. Moller, Shot noise Cox processes, Adv. in Appl. Probab. 35 (2003), pp. 614-640.
  • [84] J. Neyman, Certain chance mechanisms involving discrete distributions, in: Classical and Contagious Discrete Distributions, G. P. Patil (Ed.), Pergamon Press, Oxford 1965, pp. 4-14.
  • [85] J. N. Perry, Negative binomial model for mosquitoes, Biometrics 40 (1984), p. 863.
  • [86] D. Pollard, Convergence of Stochastic Processes, Springer, New York 1984.
  • [87] S. D. Priest and J. A. Hudson, Discontinuity spacings in rock, Int. J. Mech. Min. Sci. Geomech. Abstr. 13 (1976), pp. 135-148.
  • [88] S. D. Priest and J. A. Hudson, Estimation of discontinuity spacing and trace length using scanline surveys, Int. J. Mech. Min. Sci. Geomech. Abstr. 18 (1981), pp. 183-197.
  • [89] M. H. Quenouille, A relation between the logarithmic, Poisson, and negative binomial series, Biometrics 5 (1949), pp. 162-164.
  • [90] A. Ramakrishnan, Some simple stochastic processes, J. Roy. Statist. Soc. Ser. B 13 (1) (1951), pp. 131-140.
  • [91] D. S. Reynolds and I. R. Savage, Random wear models in reliability theory, Adv. In Appl. Probab. 3 (1971), pp. 229-248.
  • [92] J. Rosiński, Series representations of Lévy processes from the perspective of point processes, in: Lévy Processes - Theory and Applications, O. E. Barndorff-Nielsen, T. Mikosch and S. I. Resnick (Eds.), Birkhäuser, Boston 2001, pp. 401-415.
  • [93] K. Saha and S. Paul, Bias-corrected maximum likelihood estimator of the negative binomial dispersion parameter, Biometrics 61 (2005), pp. 179-185.
  • [94] M. ¸Sahinoğlu, Compound Poisson software reliability model, IEEE Trans. Software Eng. 18 (1992), pp. 624-630.
  • [95] M. ¸Sahinoğlu, An empirical Bayesian stopping rule in testing and verification of behavioral models, IEEE Trans. Instr. Meas. 52 (2003), pp. 1428-1443.
  • [96] W. Schoutens, Lévy Processes in Finance: Pricing Financial Derivatives, Wiley, Chichester 2003.
  • [97] R. F. Serfozo, Conditional Poisson processes, J. Appl. Probab. 9 (2) (1972), pp. 288-302.
  • [98] H. S. Sichel, The estimation of the parameters of a negative binomial distribution with special reference to psychological data, Psychometrika 16 (1951), pp. 107-127.
  • [99] C. H. Sim and P. A. Lee, Simulation of negative binomial processes, J. Stat. Comput. Simul. 34 (1989), pp. 29-42.
  • [100] D. L. Snyder, Random Point Processes, Wiley, New York 1975.
  • [101] R. M. Solow, On a family of lag distributions, Econometrica 28 (1960), pp. 392-406.
  • [102] L. R. Taylor, Assessing and interpreting the spatial distributions of insect populations, Ann. Rev. Entomol. 29 (1984), pp. 321-357.
  • [103] J. Vaillant, Negative binomial distributions of individuals and spatio-temporal Cox processes, Scand. J. Statist. 18 (1991), pp. 235-248.
  • [104] D. Veneziano, Basic properties and characterization of stochastically self-similar processes in Rd, Fractals 7 (1) (1999), pp. 59-78.
  • [105] L. T. Wilson and P. M. Room, Clumping patterns of fruit and arthropods in cotton, with implications for binomial sampling, Environmental Entomology 12 (1983), pp. 50-54.
  • [106] R. L. Wolpert and K. Ickstadt, Poisson/gamma random field models for spatial statistics, Biometrika 85 (1998), pp. 251-267.
  • [107] G. U. Yule, On the distribution of deaths with age when the causes of death act cumulatively, and similar frequency distributions, J. Roy. Statist. Soc. 73 (1910), pp. 26-38.
  • [108] G. U. Yule, A mathematical theory of evolution, based on the conclusions of Dr. J. C. Willis, Phil. Trans. Roy. Soc., London, B 213 (1924), pp. 21-87.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-693c8760-64c5-44fc-ac1e-2176e4f14153
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.