Aging and other distributional properties of discrete compound geometric distributions

Abstract Distributional properties of some discrete reliability classes, including the class of discrete compound geometric (D-CG) distributions, are discussed. The D-CG distribution is shown to be a subclass of the discrete strongly new worse than used class, and relations with discrete decreasing failure rate classes are considered. Upper bounds for the tail probabilities of D-CG distributions are derived. These upper bounds are of discrete Lundberg-type, and are optimal for some choices of the compounded variable. Lower bounds are also obtained. Numerical examples are given to illustrate the calculations of the bounds. The results are then applied to obtain bounds and monotonicity properties of the ruin probability in a discrete ruin model. Finally, by exploiting connections with both compound geometric and mixed Poisson distributions, reliability classifications and bounds are obtained for the equilibrium M/G/1 queue length distribution.

[1]  Stuart A. Klugman,et al.  Loss Models: From Data to Decisions , 1998 .

[2]  Jun Cai,et al.  A UNIFIED APPROACH TO THE STUDY OF TAIL PROBABILITIES OF COMPOUND DISTRIBUTIONS , 1999 .

[3]  Bengt Klefsjö A useful ageing property based on the Laplace transform , 1983 .

[4]  Elias S. W. Shiu,et al.  The Probability of Eventual Ruin in the Compound Binomial Model , 1989, ASTIN Bulletin.

[5]  I. Gertsbakh Asymptotic methods in reliability theory: a review , 1984, Advances in Applied Probability.

[6]  Gordon E. Willmot Ruin probabilities in the compound binomial model , 1993 .

[7]  H. Gerber Mathematical fun with ruin theory , 1988 .

[8]  G. Willmot Refinements and distributional generalizations of Lundberg's inequality , 1994 .

[9]  Moshe Shaked,et al.  NONHOMOGENEOUS POISSON PROCESSES AND LOGCONCAVITY , 2000, Probability in the Engineering and Informational Sciences.

[10]  G. Willmot,et al.  ON CLASSES OF LIFETIME DISTRIBUTIONS WITH UNKNOWN AGE , 2000, Probability in the Engineering and Informational Sciences.

[11]  Sheldon M. Ross,et al.  Stochastic Processes , 2018, Gauge Integral Structures for Stochastic Calculus and Quantum Electrodynamics.

[12]  Gordon E. Willmot,et al.  LUNDBERG BOUNDS ON THE TAILS OF COMPOUND DISTRIBUTIONS , 1994 .

[13]  Jun Cai,et al.  NWU property of a class of random sums , 2000, Journal of Applied Probability.

[14]  Bengt Klefsjö,et al.  HNBUE survival under some shock models , 1980 .

[15]  F. Steutel Preservation of infinite divisibility under mixing and related topics , 1972 .

[16]  William Feller,et al.  An Introduction to Probability Theory and Its Applications , 1951 .

[17]  Mark Brown Error bounds for exponential approximations of geometric convolutions , 1990 .

[18]  van K Klaas Harn,et al.  Classifying infinitely divisible distributions by functional equations , 1978 .

[19]  Tomasz Rolski,et al.  Stochastic Processes for Insurance and Finance , 2001 .

[20]  Enrico Fagiuoli,et al.  Preservation of certain classes of life distributions under Poisson shock models , 1994, Journal of Applied Probability.

[21]  Xiaodong Lin,et al.  TAIL OF COMPOUND DISTRIBUTIONS AND EXCESS TIME , 1996 .

[22]  Bengt Klefsjö,et al.  Some properties of the HNBUE and HNWUE classes of life distributions , 1982 .

[23]  J. George Shanthikumar,et al.  DFR Property of First-Passage Times and its Preservation Under Geometric Compounding , 1988 .

[24]  T. Rolski Stochastic Processes for Insurance and Finance , 1999 .

[25]  J. Grandell Mixed Poisson Processes , 1997 .

[26]  Marvin Zelen,et al.  Mathematical Theory of Reliability , 1965 .

[27]  Sلأren Asmussen,et al.  Applied Probability and Queues , 1989 .