Abstract
This paper proposes a nonstandard renewal counting process based on web Markov skeleton process, called web renewal process, which allows applications in the field of natural science and social science. Parallel with the standard renewal counting process, some properties of the web renewal process and the corresponding web renewal reward processes, are investigated. Several limit properties, including the tail of the exponential moments of the web renewal process, and the results of precise large deviations and moderate deviations for the web renewal reward processes are derived.
Similar content being viewed by others
References
Ross S M, Introduction to Probability Models, 10th ed., Academic Press, New York, 2010.
Kaas R and Tang Q, A large deviation result for aggregate claims with dependent claim occurrences, Insurance Math. Econom., 2005, 36: 251–259.
Ng K W, Tang Q, Yan J, et al., Precise large deviations for sums of random variables with consistently varying tails, J. Appl. Probab., 2004, 41(1): 93–107.
Tang Q and Tsitsiashvili G, Precise estimates for the ruin probability in finite horizon in a discrete-time model with heavy-tailed insurance and financial risks, Stochastic Processes and Their Applications, 2003, 108(2): 299–325.
Kočetova J, Leipus R, and Šiaulys J, A property of the renewal counting process with application to the finite-time ruin probability, Lithuanian Mathematical Journal, 2009, 49: 55–61.
Bi X C and Zhang S G, Precise large deviation of aggregate claims in a risk model with regression-type size-dependence, Statist. Probab. Lett., 2013, 83: 2248–2255.
Fu K and Shen X, Moderate deviations for sums of dependent claims in a size-dependent renewal risk model, Communications in Statistics — Theory and Methods, 2017, 46(7): 3235–3243.
Klüppelberg C and Mikosch T, Large deviations of heavy-tailed random sums with applications in insurance and finance, J. Appl. Probab., 1997, 34: 293–308.
Wang S and Wang W, Precise large deviations for sums of random variables with consistently varying tails in multi-risk models, J. Appl. Probab., 2007, 44: 889–900.
Baltrūnas A, Leipus R, and šiaulys J, Precise large deviation results for the total claim amount under subexponential claim sizes, Statist. Probab. Lett., 2008, 78: 1206–1214.
Shen X, Xu M, and Mills E, Precise large deviation results for sums of sub-exponential claims in a size-dependent renewal risk model, Statistics and Probability Letters, 2016, 114: 6–13.
Embrechts P, Klüppelberg C, and Mikosch T, Modeling Extremal Events for Insurance and Finance, Springer, New York, 1997.
Chen Y and Zhang W, Large deviations for random sums of negatively dependent random variables with consistently varying tails, Statistics and Probability Letters, 2007, 77: 530–538.
Tang Q, Insensitivity to negative dependence of the asymptotic behavior of precise large deviations, Electronic Journal of Probability, 2006, 11(4): 107–120.
Liu L, Precise large deviations for dependent random variables with heavy tails, Statist. Probab. Lett., 2009, 79: 1290–1298.
Lin J, The general principle for precise large deviations of heavy-tailed random sums, Statist. Probab. Lett., 2008, 78: 749–758.
Li J, Tang Q, and Wu R, Subexponential tails of discounted aggregate claims in a time-dependent renewal risk model, Advances Appl. Probab., 2010, 42: 1126–1146.
Chen Y and Yuen K C, Precise large deviations of aggregate claims in a size-dependent renewal risk model, Insurance Math. Econom., 2012, 51(2): 457–461.
Yuen Y and Yin C, Asymptotic results for tail probabilities of sums of dependent and heavy-tailed random variables, Chinese Annals of Mathematics, 2012, 33(4): 557–568.
Yang Y and Sha L, Precise large deviations for aggregate claims, Communications in Statistics — Theory and Methods, 2016, 45(10): 2801–2809.
Guo H, Wang S, and Zhang C, Precise large deviations of aggregate claims in a compound size-dependent renewal risk model, Comm. Statist. Theory Methods, 2017, 46(3): 1107–1116.
Hua Z, Song L, Lu D, et al., Precise large deviations for the difference of two sums of END random variables with heavy tails, Comm. Statist. Theory Methods, 2017, 46(2): 736–746.
Liu X, Yu C, and Gao Q, Precise large deviations of aggregate claim amount in a dependent renewal risk model, Comm. Statist. Theory Methods, 2017, 46(5): 2354–2363.
Shen X M and Zhang Y, Moderate deviations for a risk model based on the customer-arrival process, Statist. Probab. Lett., 2012, 82: 116–122.
Gao F Q, Moderate deviations for random sums of heavy-tailed random variables, Acta Math. Sin., 2007, 23: 1527–1536.
Liu L, Necessary and sufficient condtitions for moderate deviations of dependent random variables with heavy tails, Science China: Mathematics, 2010, 53: 1421–1434.
Fu K A and Shen X M, Moderate deviations for sums of dependent claims in a size-dependent renewal risk model, Communications in Statistics — Theory and Methods, 2017, 46(7): 3235–3243.
Bingham N H, Goldie C M, and Teugels J L, Regular Variation, Cambridge University Press, Cambridge, 1987.
Liu Y T, Ma Z M, and Zhou C, Web Markov skeleton processes and their applications, Tohoku Mathematical Journal, 2011, 63: 665–695.
Ma Z M, Liu Y T, and Zhou C, Further study on web Markov skeleton processes, Stochastic Analysis and Application to Finance, 2012, 313–339.
Korchevsky V M and Petrov V V, On the strong law of large numbers for sequences of dependent random variables, Vestnik St. Petersburg University, Mathematics, 2010, 43(3): 143–147.
Li R, Bi X C, and Zhang S G, Web renewal counting processes and their applications in insurance, Journal of Inequalities and Applications, 2018, 260: 1–15.
Author information
Authors and Affiliations
Corresponding authors
Additional information
This research was supported by the National Natural Science Foundation of China under Grant Nos. 11401556 and 11471304.
This paper was recommended for publication by Editor WANG Shouyang.
Rights and permissions
About this article
Cite this article
Li, R., Bi, X. & Zhang, S. Several Properties of a Nonstandard Renewal Counting Process and Their Applications. J Syst Sci Complex 33, 122–136 (2020). https://doi.org/10.1007/s11424-019-8159-3
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11424-019-8159-3