Abstract
SupposeL(X) is the law of a positive random variableX, andZ is positive and independent ofX. Admissible solution pairs (L(X),L(Z)) are sought for the in-law equation\(\hat X \cong X o Z\) ∘Z, where\(L\left( {\hat X} \right)\) is a weighted law constructed fromL(X), and ∘ is a binary operation which in some sense is increasing. The class of weights includes length biasing of arbitrary order. When ∘ is addition and the weighting is ordinary length biasing, the class of admissibleL(X) comprises the positive infinitely divisible laws. Examples are given subsuming all known specific cases. Some extensions for general order of length-biasing are discussed.
Similar content being viewed by others
References
Ahmed, A.N. & Abouammoh, A.M. (1993) Characterizations of gamma, inverse Gaussian and negative binomial distributions via their length-biased distributions.Statist. Papers 34, 167–173.
Ahsanullah, M. & Kirmani, S. (1984) A characterization of the Wald distribution.Naval Res. Logist. Quart. 31, 155–158.
Biggins, J.D. & Shanbhag, D.N. (1981) Some divisibility problems in branching processes.Math. Proc. Camb. Phil. Soc. 90, 321–330.
Feller, W. (1971)An Introduction to Probability Theory and its Applications, Vol. II, 2nd edn. Wiley, New York.
Gupta, R.C. (1975) Some characterizations of discrete distributions by properties of their moment distributions.Comm. Statist.-Theor. Meth. 4, 761–765.
Hougaard, P. (1986) Survival models for heterogeneous populations derived from stable distributions.Biometrika 73, 387–396.
Johnson, N.L., Kotz, S. & Kemp, A.W. (1993)Univariate Discrete Distributions, 2nd ed. Wiley, New York.
Katti, S.K. (1967) Infinite divisibility of integer valued random variables.Ann. Math. Statist. 38, 1306–1308.
Khattree, R. (1989) Characterization of inverse-Gaussian and gamma distributions through their length-biased distributions.IEEE Trans. Reliability 38, 610–611.
Kirmani, S. & Ahsanullah, M. (1987) A note on weighted distributions.Commun. Statist.-Theor. Meth. 16, 275–280.
Mahfoud, M. & Patil, G.P. (1982) On weighted distributions. InStatistics and Probability: Essays in Honor of C.R. Rao, eds. G. Kallianpur, P.R. Krishnaiah & J.K. Ghosh, North-Holland Publ. Co., Amsterdam, pp. 479–492.
Pakes, A.G. (1995) Characterization of discrete laws via mixed sums and Markov branching processes.Stochastic Processes Appl. 55, 285–300.
Pakes, A.G. (1994) Characterization of laws by balancing weighting against a binary operation.J. Statist. Inf. & Planning. Submitted.
Patil, G.P. & Rao, C.R. (1977) The weighted distributions: A survey of their applications. InApplications of Statistics, ed. P.R. Krishnaiah, North-Holland Publ. Co., Amsterdam, pp. 383–405.
Patil, G.P., Rao, C.R. & Ratnaparkhi, M.V. (1985) On discrete weighted distributions and their use in model choice for observed data.Comm. Statist.-Theor. Meth. 15, 907–918.
Rao, C.R. (1985) Weighted distributions arising out of methods of ascertainment: What population does a sample represent? InA Celebration of Statistics: The ISI Centenary Volume, eds. A.C. Atkinson and S.E. Fienberg, Springer-Verlag, New York, pp. 543–569.
Seshadri, V. (1993)Inverse-Gaussian Distributions: A Case Study in Natural Exponential Families. Clarendon Press, Oxford.
Steutel, F.W. (1971) On the zeros of infinitely divisible densities.Ann. Math. Statist. 42, 812–815.
Steutel, F.W. & van Harn, K. (1979) Discrete analogues of self decomposability and stability.Ann. Prob. 7, 893–899.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Pakes, A.G., Sapatinas, T. & Fosam, E.B. Characterizations, length-biasing, and infinite divisibility. Statistical Papers 37, 53–69 (1996). https://doi.org/10.1007/BF02926159
Received:
Revised:
Issue Date:
DOI: https://doi.org/10.1007/BF02926159