• 충청수학회지
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• 제22권1호
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• pp.25-30
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• 2009

In this paper, we establish some characterizations which is satisfied by the independence of the upper record values from the Weibull distribution. One characterization of several results is that $X{\in}W$ EI(1, $\alpha$), $\alpha>0$, if and only if $\frac{X_{U(m)}}{X_{U(n)}}$ and $X_{U(n)}$, $1{\leq}m are independent.

• Journal of applied mathematics & informatics
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• 제25권1_2호
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• pp.541-550
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• 2007

This paper presents characterizations of the power distribution with the parameter $\beta=1$ by the independence of the lower record values. We prove $X\;{\in}\;POW({\alpha},\;1)$ for ${\alpha}\;>\;0$, if and only if $\frac{X_{L(n)}}{X_{L(m)}}$ and $X_{L(m)}$ for $1\;{\leq}\;m\;<\;n$ are independent. And we prove that $X\;{\in}\;POW({\alpha},\;1)$ for ${\alpha}\;>\;0$, if and only if $\frac{X_{L(m)}-X_{L(m+1)}}{X_{L(m)}}$ and $X_{L(m)$ for $m\;{\geq}\;1$ are independent or $\frac{X_{L(m)}-X_{L(m+1)}}{X_{L(m+1)}}$ and $X_{L(m)}$ for $m\;{\geq}\;1$ are independent.

• 충청수학회지
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• 제20권2호
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• pp.139-146
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• 2007

In this paper, we present characterizations of the power function distribution by the independence of record values. We establish that $X{\in}$ POW(1, ${\nu}$) for ${\nu}$ > 0, if and only if $\frac{X_{L(n)}}{X_{L(n)}-X_{L(n+1)}}$ and $X_{L(n)}$ are independent for $n{\geq}1$. And we prove that $X{\in}$ POW(1, ${\nu}$) for ${\nu}$ > 0; if and only if $\frac{X_{L(n+1)}}{X_{L(n)}-X_{L(n+1)}}$ and $X_{L(n)}$ are independent for $n{\geq}1$. Also we characterize that $X{\in}$ POW(1, ${\nu}$) for ${\nu}$ > 0, if and only if $\frac{X_{L(n)}+X_{L(n+1)}}{X_{L(n)}-X_{L(n+1)}}$ and $X_{L(n)}$ are independent for $n{\geq}1$.

• Journal of applied mathematics & informatics
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• 제26권1_2호
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• pp.437-443
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• 2008

In this paper, we establish detailed characterizations of the Weibull distribution by the independence of the upper record values. We prove that X $\in$ W EI($\alpha$), if and only if $\frac{X_{U(n)}}{X_{U(n+1)}+X_{U(n)}}$ and $X_{U(n+1)}$ are independent for n $\geq$ 1. And we show that X $\in$ W EI($\alpha$), if and only if $\frac{X_{U(n+1)}-X_{U(n)}}{X_{U(n+1)}+X_{U(n)}}$ and $X_{U(n+1)}$ are independent for n $\geq$ 1.

• Journal of applied mathematics & informatics
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• 제29권5_6호
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• pp.1435-1451
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• 2011

In this paper the estimation of the parameters as well as survival and hazard functions are presented for the two-parameter Pareto distribution by using Bayesian and non-Bayesian approaches under upper record values. Maximum likelihood estimation (MLE) and interval estimation are derived for the parameters. Bayes estimators of reliability performances are obtained under symmetric (Squared error) and asymmetric (Linex and general entropy (GE)) losses, when two parameters have discrete and continuous priors, respectively. Finally, two numerical examples with real data set and simulated data, are presented to illustrate the proposed method. An algorithm is introduced to generate records data, then a simulation study is performed and different estimates results are compared.

• 대한수학회논문집
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• 제16권2호
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• pp.287-290
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• 2001

Let X$_1$, X$_2$, … be a sequence of independent and identically distributed random variables with continuous cumulative distribution function F(x). X(sub)j is an upper record value of this sequence if X(sub)j > max {X$_1$, X$_2$, …, X(sub)j-1}. We define u(n) = min {j│j > u(n-1), X(sub)j > X(sub)u(n-1), n $\geq$ 2} with u(1) = 1. Then F(x) = 1 - e(sup)-x/c, x > 0 if and only if E[X(sub)n(n+1) - X(sub)u(n)│X(sub)u(m) = y] = c or E[X(sub)u(n+2) - X(sub)u(n)│X(sub)u(m) = y] = 2c, n $\geq$ m+1.

• Journal of the Korean Data and Information Science Society
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• 제23권6호
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• pp.1249-1257
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• 2012

In this paper, we derive maximum likelihood estimators (MLEs) and approximate MLEs (AMLEs) of the unknown parameters in a generalized half logistic distribution when the data are upper record values. As an illustration, we examine the validity of our estimation using real data and simulated data. Finally, we compare the proposed estimators in the sense of the mean squared error (MSE) through a Monte Carlo simulation for various record values of size.

• Journal of applied mathematics & informatics
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• 제27권3_4호
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• pp.955-961
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• 2009

In this paper, we establish some characterizations which is satisfied by the independence of the upper record values from the Pareto distribution. We prove that $X\;{\in}\;PAR(1,\;{\beta})$, $\beta$ > 0, if and only if $\frac{X_{U(n)}}{X_{U(m)}}$ and $X_{U(m)}$, $1\;{\le}\;m\;<\;n$ are independent. We show that $X\;{\in}\;PAR(1,\;{\beta})$, $\beta$ > 0 if and only if $\frac{X_{U(n)}+X_{U{(n+1)}}}{X_{U(n)}}$ and $X_{U(n)}$, $n\;{\ge}\;1$ are independent. And we characterize that $X\;{\in}\;PAR(1,\;{\beta})$, $\beta$ > 0, if and only if $\frac{X_{U(n)}}{X_{U(n)}+X_{U{(n+1)}}}$ and $X_{U(n)}$, $n\;{\ge}\;1$ are independent.

• Communications for Statistical Applications and Methods
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• 제20권5호
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• pp.405-416
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• 2013

This paper develops a maximum profile likelihood estimator of unknown parameters of the exponentiated Gumbel distribution based on upper record values. We propose an approximate maximum profile likelihood estimator for a scale parameter. In addition, we derive Bayes estimators of unknown parameters of the exponentiated Gumbel distribution using Lindley's approximation under symmetric and asymmetric loss functions. We assess the validity of the proposed method by using real data and compare these estimators based on estimated risk through a Monte Carlo simulation.

• 충청수학회지
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• 제21권2호
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• pp.279-285
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• 2008

This paper presents some characterizations of the Weibull distribution by the independence of record values. We prove that $X{\sim}Weibull(1,{\alpha})$, ${\alpha}>0$ if and only if $\frac{X_{U(n+1)}}{X_{U(n+1)}-X_{U(n)}}$ and $X_{U(n+1)}$ for $n{\geq}1$ are independent. We show that $X{\sim}Weibull(1,{\alpha})$, ${\alpha}>0$ if and only if $\frac{X_{U(n+1)}}{X_{U(n+1)}-X_{U(n)}}$ and $X_{U(n+1)}$ for $n{\geq}1$ are independent. And we establish that $X{\sim}Weibull(1,{\alpha})$, ${\alpha}>0$ if and only if $\frac{X_{U(n+1)}+X_{U(n)}}{X_{U(n+1)}-X_{U(n)}}$ and $X_{U(n+1)}$ for $n{\geq}1$ are independent.