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ON STRONG LAWS OF LARGE NUMBERS FOR 2-DIMENSIONAL POSITIVELY DEPENDENT RANDOM VARIABLES

  • Kim, Tae-Sung;Beak, Hoh-Yoo;Seo, Hye-Young
    • Bulletin of the Korean Mathematical Society
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    • v.35 no.4
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    • pp.709-718
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    • 1998
  • In this paper we obtain strong laws of large numbers for 2-dimensional arrays of random variables which are either pairwise positive quadrant dependent or associated. Our results imply extensions of Etemadi`s strong laws of large numbers for nonnegative random variables to the 2-dimensional case.

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Application of Radiations for Random Noise (형광물질의 방사선을 응용한 Random Nois)

  • J. K. Lee
    • 전기의세계
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    • v.13 no.3
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    • pp.8-12
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    • 1964
  • The study of the research for Random process have been recently increasing rapidly. There are many methods in generating of Random signal, however, mainly these are dependent upon utilizing of hot noise of resistance and noise of discharge tube. Consequently, it is not easy to obtain of Random Noise of stabilized low frequency. Therefore, I like to study over the result of principle and design in the method of obtaining the Random Noise with faint radiations of fluoresence materials.

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Choquet expected values of fuzzy number-valued random variables and their applications (퍼지수치 확률변수의 쇼케이 기댓값과 그 응용)

  • Lee, Chae-Jang;Kim, Tae-Kyun
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2004.04a
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    • pp.394-397
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    • 2004
  • In this paper, we consider interval number-valued random variables and fuzzy number-valued random variables and discuss Choquet integrals of them. Using these properties, we define the Choquet expected value of fuzzy number-valued random variables which is a natural generalization of the Lebesgue expected value of Lebesgue expected value of fuzzy random variables. Furthermore, we discuss some application of them.

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THE CONVERGENCE RATES IN THE ASYMMETRIC LAWS OF LARGE NUMBER FOR NEGATIVELY ASSOCIATED RANDOM FIELDS

  • Ko, Mi-Hwa
    • Honam Mathematical Journal
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    • v.34 no.2
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    • pp.209-217
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    • 2012
  • Convergence rates in the law of large numbers for i.i.d. random variables have been generalized by Gut[Gut, A., 1978. Marc inkiewicz laws and convergence rates in the law of large numbers for random variables with multidimensional indices, Ann. Probab. 6, 469-482] to random fields with all indices having the same power in the normalization. In this paper we generalize these convergence rates to the identically distributed and negatively associated random fields with different indices having different power in the normalization.

ON THE ALMOST SURE CONVERGENCE OF WEIGHTED SUMS OF 2-DIMENSIONAL ARRAYS OF POSITIVE DEPENDENT RANDOM VARIABLES

  • Kim, Tae-Sung;Baek, Ho-Yu;Han, Kwang-Hee
    • Communications of the Korean Mathematical Society
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    • v.14 no.4
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    • pp.797-804
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    • 1999
  • In this paper we derive the almost sure convergence of weighted sums of 2-dimensional arrays of random variables which are either pairwise positive quadrant dependent or associated. Our re-sults imply and extension of Etemadi's(1983) strong laws of large numbers for weighted sums of nonnegative random variables to the 2-dimensional case.

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A CLT FOR A SEQUENCE OF RANDOM FIELDS ON A RESTRICTED INDEXED SET

  • JEON T. I.
    • Journal of applied mathematics & informatics
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    • v.18 no.1_2
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    • pp.441-453
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    • 2005
  • In this article we will introduce a real valued random field on a restricted indexed set and construct a classical asymptotic limit theorems on them. We will survey the basic properties of weakly dependent random processes and investigate two major mixing conditions for sequences of random variables. The concepts of weakly dependent sequence of random variables will be generalized to the case of random fields. Finally we will construct a central limit theorem and prove it.

ASYMPTOTIC DEPENDENCE BETWEEN RANDOM CENTRAL QUASI-RANGES AND RANDOM EMPIRICAL QUANTILES

  • Nigm, E.M.
    • Journal of applied mathematics & informatics
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    • v.16 no.1_2
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    • pp.289-302
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    • 2004
  • The asymptotic dependence between the central quasi-ranges and empirical quantiles was studied. The asymptotic dependence are obtained when the sample size is a positive integer valued random variable (r. v.). The dependence conditions and limit forms are obtained under generl conditions such as : the interrelation of the basic variables (the original random sample) and the random sample size is not restricted. In additition the normalizing constants do not depend on the random size.

NOTES ON RANDOM FIXED POINT THEOREMS

  • Cho Y.J.;Khan M. Firdosh;Salahuddin Salahuddin
    • The Pure and Applied Mathematics
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    • v.13 no.3 s.33
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    • pp.227-236
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    • 2006
  • The purpose of this paper is to establish a random fixed point theorem for nonconvex valued random multivalued operators, which generalize known results in the literature. We also derive a random coincidence fixed point theorem in the noncompart setting.

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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.
    • Communications of the Korean Mathematical Society
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    • v.13 no.4
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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 THE COMPLETE CONVERGENCE FOR WEIGHTED SUMS OF NEGATIVELY SUPERADDITIVE DEPENDENT RANDOM VARIABLES

  • SEO, HYE-YOUNG;SHII, DA-LI;BAEK, JONG-IL
    • Journal of applied mathematics & informatics
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    • v.37 no.3_4
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    • pp.207-217
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    • 2019
  • We are presented of several basic properties for negatively superadditive dependent(NSD) random variables. By using this concept we are obtained complete convergence for maximum partial sums of rowwise NSD random variables. These results obtained in this paper generalize a corresponding ones for independent random variables and negatively associated random variables.