scholarly journals Estimating tail probabilities of random sums of scale mixture of phase-type random variables

2021 ◽  
Keyword(s):  
Random Variables ◽  
Scale Mixture ◽  
Random Sums ◽  
Tail Probabilities ◽  
Phase Type

scholarly journals Precise Large Deviations for Random Sums of END Random Variables with Dominated Variation

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Yu Chen ◽  
Zhihui Qu
Keyword(s):  
Asymptotic Behavior ◽  
Large Deviations ◽  
Risk Model ◽  
Random Variables ◽  
Random Sums ◽  
Tail Probabilities ◽  
Precise Large Deviations ◽  
Surplus Process ◽  
End Random Variables ◽  
Renewal Risk

We investigate the precise large deviations for random sums of extended negatively dependent random variables with long and dominatedly varying tails. We find out that the asymptotic behavior of precise large deviations of random sums is insensitive to the extended negative dependence. We apply the results to a generalized dependent compound renewal risk model including premium process and claim process and obtain the asymptotic behavior of the tail probabilities of the claim surplus process.

Asymptotic tail behaviour of phase-type scale mixture distributions

2017 ◽  
Vol 12 (2) ◽  
pp. 412-432 ◽  
Author(s):  
Leonardo Rojas-Nandayapa ◽  
Wangyue Xie
Keyword(s):  
Random Variables ◽  
Random Variable ◽  
Independent Random Variables ◽  
Mixture Distributions ◽  
Scale Mixture ◽  
Domains Of Attraction ◽  
Heavy Tailed Distributions ◽  
Tail Behaviour ◽  
Phase Type ◽  
Heavy Tailed

AbstractWe consider phase-type scale mixture distributions which correspond to distributions of a product of two independent random variables: a phase-type random variable Y and a non-negative but otherwise arbitrary random variable S called the scaling random variable. We investigate conditions for such a class of distributions to be either light- or heavy-tailed, we explore subexponentiality and determine their maximum domains of attraction. Particular focus is given to phase-type scale mixture distributions where the scaling random variable S has discrete support – such a class of distributions has been recently used in risk applications to approximate heavy-tailed distributions. Our results are complemented with several examples.

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