• 제목/요약/키워드: negatively quadrant dependent 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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On Complete Convergence for Weighted Sums of Pairwise Negatively Quadrant Dependent Sequences

  • Ko, Mi-Hwa
    • Communications for Statistical Applications and Methods
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    • 제19권2호
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    • pp.247-256
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    • 2012
  • In this paper we prove the complete convergence for weighted sums of pairwise negatively quadrant dependent random variables. Some results on identically distributed and negatively associated setting of Liang and Su (1999) are generalized and extended to the pairwise negative quadrant dependence case.

THE ALMOST SURE CONVERGENCE OF WEIGHTED AVERAGES UNDER NEGATIVE QUADRANT DEPENDENCE

  • Ryu, Dae-Hee
    • Journal of applied mathematics & informatics
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    • 제27권3_4호
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    • pp.885-893
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    • 2009
  • In this paper we study the strong law of large numbers for weighted average of pairwise negatively quadrant dependent random variables. This result extends that of Jamison et al.(Convergence of weight averages of independent random variables Z. Wahrsch. Verw Gebiete(1965) 4 40-44) to the negative quadrant dependence.

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On the Probability Inequalities under Linearly Negatively Quadrant Dependent Condition

  • Baek, Jong Il;Choi, In Bong;Lee, Seung Woo
    • Communications for Statistical Applications and Methods
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    • 제10권2호
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    • pp.545-552
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    • 2003
  • Let X$_1$, X$_2$, … be real valued random variables under linearly negatively quadrant dependent (LNQD). In this paper, we discuss the probability inequality of ennett(1962) and Hoeffding(1963) under some suitable random variables. These results are to extend Theorem A and B to LNQD random variables. Furthermore, let ζdenote the pth quantile of the marginal distribution function of the $X_i$'s which is estimated by a smooth estima te $ζ_{pn}$, on the basis of X$_1$, X$_2$, …$X_n$. We establish a convergence of $ζ_{pn}$, under Hoeffding-type probability inequality of LNQD.

On the Negative Quadrant Dependence in Three Dimensions

  • Ko, Mi-Hwa;Kim, Tae-Sung
    • 호남수학학술지
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    • 제25권1호
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    • pp.117-127
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    • 2003
  • In this note we perform an extreme point analysis on two natural definitions of negative quadrant dependence of three random variables and examine how different these two notions of dependence. We also characterize some special distributions which are both negatively lower orthant dependent and negatively upper orthant dependent.

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