• 제목/요약/키워드: linearly positive quadrant dependence

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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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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 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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A functional central limit theorem for positively dependent random fields

  • Tae Sung Kim;Eun Yang Seok
    • 대한수학회논문집
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    • 제11권1호
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    • pp.265-272
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    • 1996
  • In this note we prove a functional central limit theorem for linearly positive quadrant dependent(LPQD) random fields, satisfying some assumption on covariances and the moment condition $\sup_{n \in \Zeta^d} E$\mid$S_n$\mid$^{2+\rho} < \infty$ for some $\rho > 0$. We also apply this notion to random measures.

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PRECISE ASYMPTOTICS FOR THE MOMENT CONVERGENCE OF MOVING-AVERAGE PROCESS UNDER DEPENDENCE

  • Zang, Qing-Pei;Fu, Ke-Ang
    • 대한수학회보
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    • 제47권3호
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    • pp.585-592
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    • 2010
  • Let {$\varepsilon_i:-{\infty}$$\infty$} be a strictly stationary sequence of linearly positive quadrant dependent random variables and $\sum\limits\frac_{i=-{\infty}}^{\infty}|a_i|$<$\infty$. In this paper, we prove the precise asymptotics in the law of iterated logarithm for the moment convergence of moving-average process of the form $X_k=\sum\limits\frac_{i=-{\infty}}^{\infty}a_{i+k}{\varepsilon}_i,k{\geq}1$

A FUNCTIONAL CENTRAL LIMIT THEOREM FOR MULTIVARIATE LINEAR PROCESS WITH POSITIVELY DEPENDENT RANDOM VECTORS

  • KO, MI-HWA;KIM, TAE-SUNG;KIM, HYUN-CHULL
    • 호남수학학술지
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    • 제27권2호
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    • pp.301-315
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    • 2005
  • Let $\{A_u,\;u=0,\;1,\;2,\;{\cdots}\}$ be a sequence of coefficient matrices such that ${\sum}_{u=0}^{\infty}{\parallel}A_u{\parallel}<{\infty}$ and ${\sum}_{u=0}^{\infty}\;A_u{\neq}O_{m{\times}m}$, where for any $m{\times}m(m{\geq}1)$, matrix $A=(a_{ij})$, ${\parallel}A{\parallel}={\sum}_{i=1}^m{\sum}_{j=1}^m{\mid}a_{ij}{\mid}$ and $O_{m{\times}m}$ denotes the $m{\times}m$ zero matrix. In this paper, a functional central limit theorem is derived for a stationary m-dimensional linear process ${\mathbb{X}}_t$ of the form ${\mathbb{X}_t}={\sum}_{u=0}^{\infty}A_u{\mathbb{Z}_{t-u}}$, where $\{\mathbb{Z}_t,\;t=0,\;{\pm}1,\;{\pm}2,\;{\cdots}\}$ is a stationary sequence of linearly positive quadrant dependent m-dimensional random vectors with $E({\mathbb{Z}_t})={{\mathbb{O}}$ and $E{\parallel}{\mathbb{Z}_t}{\parallel}^2<{\infty}$.

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