• Title/Summary/Keyword: random sum

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ON THE COMPLETE MOMENT CONVERGENCE OF MOVING AVERAGE PROCESSES GENERATED BY ρ*-MIXING SEQUENCES

  • Ko, Mi-Hwa;Kim, Tae-Sung;Ryu, Dae-Hee
    • Communications of the Korean Mathematical Society
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    • v.23 no.4
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    • pp.597-606
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    • 2008
  • Let {$Y_{ij}-{\infty}\;<\;i\;<\;{\infty}$} be a doubly infinite sequence of identically distributed and ${\rho}^*$-mixing random variables with zero means and finite variances and {$a_{ij}-{\infty}\;<\;i\;<\;{\infty}$} an absolutely summable sequence of real numbers. In this paper, we prove the complete moment convergence of {${\sum}^n_{k=1}\;{\sum}^{\infty}_{i=-{\infty}}\;a_{i+k}Y_i/n^{1/p}$; $n\;{\geq}\;1$} under some suitable conditions. We extend Theorem 1.1 of Li and Zhang [Y. X. Li and L. X. Zhang, Complete moment convergence of moving average processes under dependence assumptions, Statist. Probab. Lett. 70 (2004), 191.197.] to the ${\rho}^*$-mixing case.

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

  • Zang, Qing-Pei;Fu, Ke-Ang
    • Bulletin of the Korean Mathematical Society
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    • v.47 no.3
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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 $X_k=\sum\limits\frac_{i=-{\infty}}^{\infty}a_{i+k}{\varepsilon}_i,k{\geq}1$

BERRY-ESSEEN BOUNDS OF RECURSIVE KERNEL ESTIMATOR OF DENSITY UNDER STRONG MIXING ASSUMPTIONS

  • Liu, Yu-Xiao;Niu, Si-Li
    • Bulletin of the Korean Mathematical Society
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    • v.54 no.1
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    • pp.343-358
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    • 2017
  • Let {$X_i$} be a sequence of stationary ${\alpha}-mixing$ random variables with probability density function f(x). The recursive kernel estimators of f(x) are defined by $$\hat{f}_n(x)={\frac{1}{n\sqrt{b_n}}{\sum_{j=1}^{n}}b_j{^{-\frac{1}{2}}K(\frac{x-X_j}{b_j})\;and\;{\tilde{f}}_n(x)={\frac{1}{n}}{\sum_{j=1}^{n}}{\frac{1}{b_j}}K(\frac{x-X_j}{b_j})$$, where 0 < $b_n{\rightarrow}0$ is bandwith and K is some kernel function. Under appropriate conditions, we establish the Berry-Esseen bounds for these estimators of f(x), which show the convergence rates of asymptotic normality of the estimators.

Symbolic Reliability Evaluation of Combinational Logic Circuit (조합논리회로의 기호적 신뢰도 계정)

  • 오영환
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.7 no.1
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    • pp.25-28
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    • 1982
  • A method for finding the symbolic reliability expressision of a conbinational logic circuit is presented. The evaluation of the probabilities of the outputs can be symbolically evaluated by the Boolean operation named sharp operation, provided that every input of such a circuit can be treated as random variables with values set(0, 1) and the output of a circuit can be represented by a Boolean sum of produt expression.

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COMPLETE MOMENT CONVERGENCE OF MOVING AVERAGE PROCESSES WITH DEPENDENT INNOVATIONS

  • Kim, Tae-Sung;Ko, Mi-Hwa;Choi, Yong-Kab
    • Journal of the Korean Mathematical Society
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    • v.45 no.2
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    • pp.355-365
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    • 2008
  • Let ${Y_i;-\infty<i<\infty}$ be a doubly infinite sequence of identically distributed and $\phi$-mixing random variables with zero means and finite variances and ${a_i;-\infty<i<\infty}$ an absolutely summable sequence of real numbers. In this paper, we prove the complete moment convergence of ${{\sum}_{k=1}^{n}\;{\sum}_{i=-\infty}^{\infty}\;a_{i+k}Y_i/n^{1/p};n\geq1}$ under some suitable conditions.

A Clarification of the Cauchy Distribution

  • Lee, Hwi-Young;Park, Hyoung-Jin;Kim, Hyoung-Moon
    • Communications for Statistical Applications and Methods
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    • v.21 no.2
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    • pp.183-191
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    • 2014
  • We define a multivariate Cauchy distribution using a probability density function; subsequently, a Ferguson's definition of a multivariate Cauchy distribution can be viewed as a characterization theorem using the characteristic function approach. To clarify this characterization theorem, we construct two dependent Cauchy random variables, but their sum is not Cauchy distributed. In doing so the proofs depend on the characteristic function, but we use the cumulative distribution function to obtain the exact density of their sum. The derivation methods are relatively straightforward and appropriate for graduate level statistics theory courses.

Krawtchouk Polynomial Approximation for Binomial Convolutions

  • Ha, Hyung-Tae
    • Kyungpook Mathematical Journal
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    • v.57 no.3
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    • pp.493-502
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    • 2017
  • We propose an accurate approximation method via discrete Krawtchouk orthogonal polynomials to the distribution of a sum of independent but non-identically distributed binomial random variables. This approximation is a weighted binomial distribution with no need for continuity correction unlike commonly used density approximation methods such as saddlepoint, Gram-Charlier A type(GC), and Gaussian approximation methods. The accuracy obtained from the proposed approximation is compared with saddlepoint approximations applied by Eisinga et al. [4], which are the most accurate method among higher order asymptotic approximation methods. The numerical results show that the proposed approximation in general provide more accurate estimates over the entire range for the target probability mass function including the right-tail probabilities. In addition, the method is mathematically tractable and computationally easy to program.

PRECISE ASYMPTOTICS IN COMPLETE MOMENT CONVERGENCE FOR DEPENDENT RANDOM VARIABLE

  • Han, Kwang-Hee
    • Honam Mathematical Journal
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    • v.31 no.3
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    • pp.369-380
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    • 2009
  • Let $X,X_1,X_2,\;{\cdots}$ be identically distributed and negatively associated random variables with mean zeros and positive, finite variances. We prove that, if $E{\mid}X_1{\mid}^r$ < ${\infty}$, for 1 < p < 2 and r > $1+{\frac{p}{2}}$, and $lim_{n{\rightarrow}{\infty}}n^{-1}ES^2_n={\sigma}^2$ < ${\infty}$, then $lim_{{\epsilon}{\downarrow}0}{\epsilon}^{{2(r-p}/(2-p)-1}{\sum}^{\infty}_{n=1}n^{{\frac{r}{p}}-2-{\frac{1}{p}}}E\{{{\mid}S_n{\mid}}-{\epsilon}n^{\frac{1}{p}}\}+={\frac{p(2-p)}{(r-p)(2r-p-2)}}E{\mid}Z{\mid}^{\frac{2(r-p)}{2-p}}$, where $S_n\;=\;X_1\;+\;X_2\;+\;{\cdots}\;+\;X_n$ and Z has a normal distribution with mean 0 and variance ${\sigma}^2$.

Staistical analysis and measurements of array receivers for use in optical communication (광통신에 이용되는 배열 수신기의 측정과 통계적 해석)

  • Sung, Pyung-Shik
    • Journal of the Korea Computer Industry Society
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    • v.8 no.1
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    • pp.57-64
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    • 2007
  • In the paper, considers the design of a detection system, consisting of n identical detectors, to process the sum of spatially invariant signal fields $S_\jmath(t)$, an additive, homogeneous, spatially isotropic (in the array plane x). covariance_separable, and zero_mean Gaussian random noise field n(t, x). consisting of arrey receivers for use in optical communication through optimum turbulence atmosphers and covriance_circuit, staistical analysis through detection the maximum gain are compared with analysis theoretical analysis theoretical value and get through experimental measured value, as well the measured valus are confirmed to agreement joint Gaussian theoretical curves.

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