Weak convergence of compound stochastic process,I |
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Authors: | Donald L. Iglehart |
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Affiliation: | Department of Statistics, Stanford University, Stanford, Calif. 94305, U.S.A. |
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Abstract: | Compound stochastic processes are constructed by taking the superpositive of independent copies of secondary processes, each of which is initiated at an epoch of a renewal process called the primary process. Suppose there are M possible k-dimensional secondary processes {ξv(t):t?0}, v=1,2,…,M. At each epoch of the renewal process {A(t):t?0} we initiate a random number of each of the M types. Let ml:l?1} be a sequence of M-dimensional random vectors whose components specify the number of secondary processes of each type initiated at the various epochs. The compound process we study is , where the ξvlj() are independent copies of ξv,mlv is the vth component of m and {τl:l?1} are the epochs of the renewal process. Our interest in this paper is to obtain functional central limit theorems for {Y(t):t?0} after appropriately scaling the time parameter and state space. A variety of applications are discussed. |
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Keywords: | 6030 Compound stochastic processes functional central limit theorem invariance principle weak convergence |
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