The negative-binomial sum is an extension of a geometric sum. It has been arisen from the necessity to resolve practical problems in telecommunications, network analysis, stochastic finance and insurance mathematics, etc. Up to the present, the topics related to negative-binomial sums like asymptotic distributions and rates of convergence have been investigated by many mathematicians. However, in a lot of various situations, the results concerned the rates of convergence for negative-binomial sums are still restrictive. The main purpose of this paper is to establish some weak limit theorems for negative-binomial sums of independent, identically distributed (i.i.d.) random variables via Gnedenko's Transfer Theorem originated by Gnedenko and Fahim (1969). Using Zolotarev's probability metric, the rate of convergence in weak limit theorems for negativebinomial sum are established. The received results are the rates of convergence in weak limit theorem for partial sum of i.i.d random variables related to symmetric stable distribution (Theorem 1), and asymptotic distribution together with the convergence rates for negative-binomial sums of i.i.d. random variables concerning to symmetric Linnik laws and Generalized Linnik distribution (Theorem 2 and Theorem 3). Based on the results of this paper, the analogous results for geometric sums of i.i.d. random variables will be concluded as direct consequences. However, the article has just been solved for the case of 1 <a < 2; it is quite hard to estimate in the case of a 2 (0;1) via the Zolotarev's probability metric.
Mathematics Subject Classification 2010: 60G50; 60F05; 60E07.
We follow the notations used in 1 . A random variable is said to have negative-binomial distribution with two parameters an d , if its probability mass function is given in form
Let be a sequence of independent, identically distributed (i.i.d.) random variables, independent of Then, the sum
It is well-known that the topics related to negative-binomial sums have become the interesting research objects in probability theory. It has many applications in telecommunications, network analysis, stochastic finance and insurance mathematics, etc . Recently, problems concerning with negative-binomial sums have been investigated by Vellaisamy and Upadhye (2009), Yakumiv (2011), Sunklodas (2015), Sheeja and Kumar (2017), Giang and Hung (2018), Omair et al . (2018), Hung and Hau (2018), etc . (see 9 , 1 , 8 , 7 , 6 , 5 , 4 ). In many situations, some problems on the negative-binomial sums have not been fully studied yet, therefore its applications are still restrictive.
Therefore, the main aim of article is to establish weak limit theorems for normalized negative-binomial sums via Gnedenkoâ€™s Transfer Theorem (see 10 for more details), where and for any Moreover, using Zolotarevâ€™s probability metric, the rate of convergence in weak limit theorem for normalized negative-binomial sum will be estimated. It is clear that corresponding results for normalized geometric sums of i.i.d. random variables will be concluded when
From now on, the symbols and denote the convergence in distribution and equality in distribution, respectively. The set of real numbers is denoted by and we will denote by the set of natural numbers.
We denote by the set of random variables defined on a probability space and denote by the set of all real-valued, bounded, uniformly continuous functions defined on with norm . Moreover, for any and let us set
where is derivative function of order of Then, the Zolotarevâ€™s probability metric will be recalled as follows
Let and Zolotarev's probability metric of order 2 is defined by
1. Zolotarev's probability metric is an ideal metric of order , i.e ., for any we have
and with is independent of and we get
2. If as then as
3. Let and be two independent sequences of i.i.d. random variables (in each sequence). Then, for all
The following lemma states the most important property of Zolotarev's probability metric which will be used in proofs of our results.
Lemma 1. Let with and . The n
Proof. For any and by Mean Value Theorem we have
where is between and Moreover, since one has
Hence, we obtain following inequality
Therefore, for all we get
The proof is straight-forward.
In the sequel, we shall recall several well-known distributions which are related to limit distributions of non-randomly sums and negative-binomial sums of i.i.d. random variables.
We follow the notations used in ( 1 , page 204). A random variable is said to have symmetric stable distribution with two parameters and denoted by if its characteristic function is given in form
A random variable is said to have symmetric Linnik distribution with two parameters and denoted by if its characteristic function is given by (see 1 , page 199)
A random variable is said to have G eneralized Linnik distribution with three parameters and denoted by if its characteristic function is given as (see 1 , page 216)
From now on, let be a fixed natural number, for any, and We first prove the following theorem.
Theorem 1. Let be a sequence of i.i.d. random variables with Assume that and with Then ,
Proof. Let be a sequence of i.i.d. copies of Then, it is clear that
Based on the ideality of order of Zolotarev's probability metric and according to Lemma 1, it follows that
The proof is straightforward.
Remark 1 . Since according to ( 14 , Corollary 5, page 305) then Moreover, based on the finiteness of and , a weak limit theorem for normalized non-random sum will be state d from Theorem 1 as follows
Proposition 1 . Let Then ,
Proof. Since the characteristic function of is given by
Hence, the characteristic function of is defined by
Letting we conclude that
This finishes the proof.
Using Gnedenkoâ€™s Transfer Theorem (see 10 ), a weak limit theorem for negative-binomial sum of i.i.d. random variables will be established as follows
Theorem 2. Let be a sequence of i.i.d. random variables with Let independent o f for all . Then,
Proof. According to Proposition 1, we have
where and the density function of Gamma random variable is defined by
Furthermore, by Theorem 1, one has
where whose characteristic function is given by
On account of Gnedenkoâ€™s Transfer Theorem (see 10 ), it follows that
where is a random variable whose characteristic function is defined by
Let us set then
The proof is immediate.
Next, the rate of convergence in Theorem 2 will be estimated via Zolotarevâ€™s probability metric by the following theorem.
Theorem 3. Let be a sequence of i.i.d. random variables with Assume that independent of for all . Then,
Proof. Let be a sequence of independent, symmetric Linnik distributed random variables with parameters and independent of . Then, the characteristic function of sum is defined by
Hence, the characteristic function of sum will be defined as follows
On account of ideality of Zolotarev's probability metric of order it follows that
Since with by Proposition 4.3.18 in ( 1 , page 212), we have
On account of Lemma 1, one has
The proof is complete.
Remark 2 . From Theorem 1, a weak limit theorem for normalized negative-binomial random sum will be stated as follows
In some situations, it is quite hard to establish the limiting distributions for negative-binomial sums of i.i.d. random variables. Meanwhile, if the limiting distribution of the partial sum is stated, the limiting distribution of corresponding negative-binomial sum will be established by the Gnedenkoâ€™s Transfer Theorem (see 10 ). Thus, in this paper, the asymptotic behaviors of normalized negative-binomial random sums of i.i.d. random variables have been established via Gnedenkoâ€™s Transfer Theorem (Theorem 2).
Moreover, the mathematical tools have been used in study of convergence rates in limit theorems of probability theory including method of characteristic functions, method of linear operators, method of probability metrics and Steinâ€™s method, etc. Especially, the method of probability metrics is more effective. U sing the Zolotarevâ€™s probability metric, the rates of convergence in weak limit theorem for partial sum and negative-binomial sum of i.i.d. random variables are estimated (Theorem 1 and Theorem 2).
It is worth pointing out that the Zolotarevâ€™s probability metric used in this paper is an ideal metric, so it is easy to estimate approximations concerning random sums of random variables. Furthermore, this metric can be compared with well-known metrics like Kolmogorov metric, total variation metric, LĂ©vy-Prokhorov metric and probability metric based on Trotter operator, etc .
A negative-binomial distributed random variable with two parameters and will reduce a geometric distributed random variable with parameter when . Hence, the analogous results for geometric sums of i.i.d. random variables will be concluded as direct consequences from this paper.
However, the article has just been solved for the case of it is very difficult to estimate in the case of via the Zolotarev's probability metric, but it will be considered in near future. Analogously, we can estimate the rates of convergence for negative-binomial sums of i.i.d. random variables for cases of and .
The authors declare that they have no competing interests.
All authors contributed equally and significantly to this work. All authors drafted the manuscript, read and approved the final version of the manuscript.
The authors wish to express gratitude to mathematicians for sending their value published articles.
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