On Strong Convergence for Weighted Sums of a Class of Random Variables | Zendy

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On Strong Convergence for Weighted Sums of a Class of Random Variables

Author(s) -

Aiting Shen

Publication year - 2013

Publication title -

abstract and applied analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.228

H-Index - 56

eISSN - 1687-0409

pISSN - 1085-3375

DOI - 10.1155/2013/216236

Subject(s) - convergence (economics) , sequence (biology) , algorithm , independent and identically distributed random variables , type (biology) , mathematics , moment (physics) , random variable , statistics , physics , chemistry , geology , paleontology , biochemistry , classical mechanics , economics , economic growth

Let {Xn,n≥1} be a sequence of random variables satisfying the Rosenthal-type maximal inequality. Complete convergence is studied for linear statistics that are weighted sums of identically distributed random variables under a suitable moment condition. As an application, the Marcinkiewicz-Zygmund-type strong law of large numbers is obtained. Our result generalizes the corresponding one of Zhou et al. (2011) and improves the corresponding one of Wang et al. (2011, 2012)

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