• 제목/요약/키워드: Positive quadrant dependence

검색결과 22건 처리시간 0.017초

THE ORDERING OF CONDITIONALLY WEAK POSITIVE QUADRANT DEPENDENCE

  • BARK, JONG-IL;LEE, SEUNG-WOO;KIM, SO-YOUN;LEE, GIL-HWAN
    • 호남수학학술지
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    • 제28권2호
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    • pp.279-290
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    • 2006
  • In this paper, we introduced a new notion of conditionally weakly positive quadrant dependence(CWPQD) between two random variables and the partial ordering of CWPQD is developed to compare pairs of CWPQD random vectors. Some properties and closure under certain statistical operations are derived.

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A Family of Extended NQD Bivariate Distributions with Continuous Marginals

  • Ryu, Dae-Hee
    • Communications for Statistical Applications and Methods
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    • 제19권1호
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    • pp.85-95
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    • 2012
  • In this paper we define extended negative quadrant dependence which is weaker negative quadrant dependence and show conditions for having extended negative quadrant dependence property. We also derive generalized Farlie-Gumbel-Morgenstern uniform distributions that possess the extended quadrant dependence property.

A WEAK ORDERING OF POSITIVE DEPENDENCE STRUCTURE OF STOCHASTIC PROCESSES

  • Ryu, Dae-Hee;Seok, Eun-Yang;Choi, In-Bong
    • Journal of applied mathematics & informatics
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    • 제5권2호
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    • pp.553-564
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    • 1998
  • In this paper we introduce a new concept of more weakly quadrant dependence of hitting times of stochastic processes. This concept is weaker than the more positively quadrant dependence of hitting times of stochastic processes. This concept is weaker than the more positively quadrant dependence and it is closed under some statistical operations of weakly positive quadrant dependence(WPQD) ordering.

A functional central limit theorem for positively dependent random vectors

  • Kim, Tae-Sung;Baek, Jong-Il
    • 대한수학회논문집
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    • 제10권3호
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    • pp.707-714
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    • 1995
  • In this note, we extend the concepts of linearly positive quadrant dependence to the random vectors and prove a functional central limit theorem for positively quadrant dependent sequence of $R^d$-valued or separable Hilbert space valued random elements which satisfy a covariance summability condition. This result is an extension of a functional central limit theorem for weakly associated random vectors of Burton et al. to positive quadrant dependence case.

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A PARTIAL ORDERING OF WEAK POSITIVE QUADRANT DEPENDENCE

  • Kim, Tae-Sung;Lee, Young-Ro
    • 대한수학회논문집
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    • 제11권4호
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    • pp.1105-1116
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    • 1996
  • A partial ordering is developed among weakly positive quadrant dependent (WPQD) bivariate random vectors. This permits us to measure the degree of WPQD-ness and to compare pairs of WPQD random vectors. Some properties and closures under certain statistical operations are derived. An application is made to measures of dependence such as Kendall's $\tau$ and Spearman's $\rho$.

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ON THE STRONG LAW OF LARGE NUMBERS FOR LINEARLY POSITIVE QUADRANT DEPENDENT RANDOM VARIABLES

  • Kim, Tae-Sung;Seo, Hye-Young
    • 대한수학회논문집
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    • 제13권1호
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    • pp.151-158
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    • 1998
  • In this note we derive inequalities of linearly positive quadrant dependent random variables and obtain a strong law of large numbers for linealy positive quardant dependent random variables. Our results imply an extension of Birkel's strong law of large numbers for associated random variables to the linear positive quadrant dependence case.

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A weakly dependence concepts of bivariate stochastic processes

  • Choi, Jeong-Yeol;Baek, Jong-Il;Youn, Eun-Ho
    • 대한수학회논문집
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    • 제11권3호
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    • pp.831-839
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    • 1996
  • In the last years there has been growing interest in concepts of positive (negative) dependence of stochastic processes such that concepts are considerable us in deriving inequalities in probability and statistics. Lehmann [7] introduced various concepts of positive(negative) dependence in the bivariate case. Stronger notions of bivariate positive(negative) dependence were later developed by Esary and Proschan [6]. Ahmed et al.[2], and Ebrahimi and Ghosh[5] obtained multivariate versions of various positive(negative) dependence as described by Lehmann[7] and Esary and Proschan[6]. Concepts of positive(negative) dependence for random variables have subsequently been extended to stochastic processes in different directions by many authors.

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On the Conditionally Independent and Positive and Negative Dependence of Bivariate Stochastic Processes

  • Baek, Jong Il;Han, Kwang Hee
    • Communications for Statistical Applications and Methods
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    • 제9권2호
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    • pp.367-379
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    • 2002
  • We introduce a new concept of $\theta$ conditionally independent and positive and negative dependence of bivariate stochastic processes and their corresponding hitting times. We have further extended this theory to stronger conditions of dependence similar to those in the literature of positive and negative dependence and developed theorems which relate these conditions. Finally we are given some examples to illustrate these concepts.

A Note on Stationary Linearly Positive Quadrant Dependent Sequences

  • Kim, Tae-Sung
    • Journal of the Korean Statistical Society
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    • 제24권1호
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    • pp.249-256
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    • 1995
  • In this note we prove an invariance principle for strictly stationary linear positive quadrant dependent sequences, satifying some assumption on the covariance structure, $0 < \sum Cov(X_1,X_j) < \infty$. This result is an extension of Burton, Dabrowski and Dehlings' invariance principle for weakly associated sequences to LPQD sequences as well as an improvement of Newman's central limit theorem for LPQD sequences.

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THE INVARIANCE PRINCIPLE FOR LINEARLY POSITIVE QUADRANT DEPENDENT RANDOM FIELDS

  • Kim, Tae-Sung;Seo, Hye-Young
    • 대한수학회지
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    • 제33권4호
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    • pp.801-811
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    • 1996
  • Let $Z^d$ denote the set of all d-tuples of integers$(d \geq 1, a positive integer)$. The points in $Z^d$ will be denoted by $\underline{m},\underline{n}$, etc., or sometime, when necessary, more explicitly by $(m_1, m_2, \cdots, m_d)$, $(n_1, n_2, \cdots, n_d)$ etc. $Z^d$ is partially ordered by stipulating $\underline{m} \underline{<}\underline{n} iff m_i \leq n_i$ for each i, $1 \leq i \leq d$.

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