• 제목/요약/키워드: negative associated random variables

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A NOTE ON THE STRONG LAW OF LARGE NUMBERS FOR WEIGHTED SUMS OF NEGATIVELY DEPENDENT RANDOM VARIABLES

  • Lee, S.W.;Kim, T.S.;Kim, H.C.
    • 대한수학회논문집
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    • 제13권4호
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    • pp.855-863
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    • 1998
  • Some conditions on the strong law of large numbers for weighted sums of negative quadrant dependent random variables are studied. The almost sure convergence of weighted sums of negatively associated random variables is also established, and then it is utilized to obtain strong laws of large numbers for weighted averages of negatively associated random variables.

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THE ALMOST SURE CONVERGENCE FOR THE IDENTICALLY DISTRIBUTED NEGATIVELY ASSOCIATED RANDOM VARIABLES WITH INFINITE MEANS

  • Kim, Hyun-Chull
    • Journal of applied mathematics & informatics
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    • 제28권1_2호
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    • pp.363-372
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    • 2010
  • In this paper we prove the almost sure convergence of partial sums of identically distributed and negatively associated random variables with infinite expectations. Some results in Kruglov[Kruglov, V., 2008 Statist. Probab. Lett. 78(7) 890-895] are considered in the case of negatively associated random variables.

COMPLETE CONVERGENCE FOR ARRAYS OF ROWWISE ASYMPTOTICALLY NEGATIVELY ASSOCIATED RANDOM VARIABLES

  • Kim, Hyun-Chull
    • 충청수학회지
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    • 제30권4호
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    • pp.411-422
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    • 2017
  • Let {$X_{ni}$, $i{\geq}1$, $n{\geq}1$} be an array of rowwise asymptotically negatively associated random variables and {$a_{ni}$, $i{\geq}1$, $n{\geq}1$} an array of constants. Some results concerning complete convergence of weighted sums ${\sum}_{i=1}^{n}a_{ni}X_{ni}$ are obtained. They generalize some previous known results for arrays of rowwise negatively associated random variables to the asymptotically negative association case.

선형 음의 사분 종속확률변수에서 가중합에 대한 수렴성 연구 (Convergence of weighted sums of linearly negative quadrant dependent random variables)

  • 이승우;백종일
    • 한국신뢰성학회지:신뢰성응용연구
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    • 제12권4호
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    • pp.265-274
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    • 2012
  • We in this paper discuss the strong law of large numbers for weighted sums of arrays of rowwise LNQD random variables by using a new exponential inequality of LNQD r.v.'s under suitable conditions and we obtain one of corollary.

A Central Limit Theorem for the Linear Process in a Hilbert Space under Negative Association

  • Ko, Mi-Hwa
    • Communications for Statistical Applications and Methods
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    • 제16권4호
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    • pp.687-696
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    • 2009
  • We prove a central limit theorem for the negatively associated random variables in a Hilbert space and extend this result to the linear process generated by negatively associated random variables in a Hilbert space. Our result implies an extension of the central limit theorem for the linear process in a real space under negative association to a simplest case of infinite dimensional Hilbert space.

On Convergence of Weighted Sums of LNQD Random

  • Kim, So-Youn;Baek, Jong-Il
    • Communications for Statistical Applications and Methods
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    • 제19권5호
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    • pp.647-654
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    • 2012
  • We discuss the strong convergence for weighted sums of linearly negative quadrant dependent(LNQD) random variables under suitable conditions and the central limit theorem for weighted sums of an LNQD case is also considered. In addition, we derive some corollaries in LNQD setting.

ON H$\grave{a}$JEK-R$\grave{e}$NYI-TYPE INEQUALITY FOR CONDITIONALLY NEGATIVELY ASSOCIATED RANDOM VARIABLES AND ITS APPLICATIONS

  • Seo, Hye-Young;Baek, Jong-Il
    • Journal of applied mathematics & informatics
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    • 제30권3_4호
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    • pp.623-633
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    • 2012
  • Let {${\Omega}$, $\mathcal{F}$, P} be a probability space and {$X_n|n{\geq}1$} be a sequence of random variables defined on it. A finite sequence of random variables {$X_n|n{\geq}1$} is said to be conditionally negatively associated given $\mathcal{F}$ if for every pair of disjoint subsets A and B of {1, 2, ${\cdots}$, n}, $Cov^{\mathcal{F}}(f_1(X_i,i{\in}A),\;f_2(X_j,j{\in}B)){\leq}0$ a.s. whenever $f_1$ and $f_2$ are coordinatewise nondecreasing functions. We extend the H$\grave{a}$jek-R$\grave{e}$nyi-type inequality from negative association to conditional negative association of random variables. In addition, some corollaries are given.