• Water Engineering Research
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• 제4권3호
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• pp.155-173
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• 2003

Parameters and quantiles of the 2-parameter generalized Pareto distribution were estimated using the methods of regular moments, modified moments, probability weighted moments, linear moments, maximum likelihood, and entropy for Monte Carlo-generated samples. The performance of these seven estimators was statistically compared, with the objective of identifying the most robust estimator. It was found that in general the methods of probability-weighted moments and L-moments performed better than the methods of maximum likelihood estimation, moments and entropy, especially for smaller values of the coefficient of variation and probability of exceedance.

• Communications for Statistical Applications and Methods
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• 제6권1호
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• pp.285-292
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• 1999

In this paper we propose the several estimators of the Lorenz curve in the Pareto distribution and obtain the bias and the mean squared error for each estimator. We compare the proposed estimators with the uniformly minimum variance unbiased estimator (UMVUE) and the maximum likelihood estimator (MLE) in terms of the mean squared error (MSE) through Monte Carlo methods and discuss the results.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2003년도 춘계학술대회
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• pp.27-37
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• 2003

In this paper, we develop noninformative priors for two parameter Pareto distribution. Specially, we derive Jeffrey's prior, probability matching prior and reference prior for the parameter of interest. In our case, the probability matching prior is only a first order and there does not exist a second order matching prior. Some simulation reveals that the matching prior performs better to achieve the coverage probability. And a real example will be given.

• 대한수학회지
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• 제32권4호
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• pp.877-888
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• 1995

Many authors have utilized a Pareto distribution because of its wide applicability in socio-economic, physical and biological phenomena.

• Communications for Statistical Applications and Methods
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• 제3권1호
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• pp.195-206
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• 1996

Several parametric estimators of the scale parameter and reliability in an assumed Pareto distribution with the presence of identified outliers are proposed, and their efficiencies are compared numerically each other.

• 응용통계연구
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• 제26권5호
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• pp.713-723
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• 2013

본 연구에서는 2-모수 파레토분포에 대해 무정보사전분포인 준거사전분포의 가정 하에서 객관적 베이지안 모수추정 절차를 제안하였다. 베이지안 추정은 깁스샘플링에 의해서 수행된다. 깁스샘플러에서 모수생성하는 방법은 형태모수는 감마분포로부터 생성하고 척도모수는 적응기각표집 알고리즘에 의해 생성한다. 제안된 베이지안 모수추정 절차는 모의실험과 자료분석에서 기존의 추정방법들인 L-적률추정법, 최우추정법, 공액사전분포 하의 주관적 베이지안 모수추정법과 비교된다.

• ETRI Journal
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• 제28권3호
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• pp.301-310
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• 2006

In mobile communications, a class of variable-complexity algorithms for convolutional decoding known as sequential decoding algorithms is of interest since they have a computational time that could vary with changing channel conditions. The Fano algorithm is one well-known version of a sequential decoding algorithm. Since the decoding time of a Fano decoder follows the Pareto distribution, which is a heavy-tailed distribution parameterized by the channel signal-to-noise ratio (SNR), buffers are required to absorb the variable decoding delays of Fano decoders. Furthermore, since the decoding time drawn by a certain Pareto distribution can become unbounded, a maximum limit is often employed by a practical decoder to limit the worst-case decoding time. In this paper, we investigate the relations between buffer occupancy, decoding time, and channel conditions in a system where the Fano decoder is not allowed to run with unbounded decoding time. A timeout limit is thus imposed so that the decoding will be terminated if the decoding time reaches the limit. We use discrete-time semi-Markov models to describe such a Fano decoding system with timeout limits. Our queuing analysis provides expressions characterizing the average buffer occupancy as a function of channel conditions and timeout limits. Both numerical and simulation results are provided to validate the analytical results.

• Journal of the Korean Data and Information Science Society
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• 제22권1호
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• pp.115-123
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• 2011

In this paper, we develop the noninformative priors for stress-strength reliability from the Pareto distributions. We develop the matching priors and the reference priors. It turns out that the second order matching prior does not match the alternative coverage probabilities, and is not a highest posterior density matching or a cumelative distribution function matching priors. Also we reveal that the one-at-a-time reference prior and Jeffreys' prior are the second order matching prior. We show that the proposed reference prior matches the target coverage probabilities in a frequentist sense through simulation study, and an example is given.

• Journal of the Korean Data and Information Science Society
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• 제23권2호
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• pp.353-363
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• 2012

In this paper, we introduce the noninformative priors such as the matching priors and the reference priors for the common scale parameter in the Pareto distributions. It turns out that the posterior distribution under the reference priors is not proper, and Jeffreys' prior is not a matching prior. It is shown that the proposed first order prior matches the target coverage probabilities in a frequentist sense through simulation study.

• Communications for Statistical Applications and Methods
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• 제16권3호
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• pp.463-477
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• 2009

극단치 분포의 모수 추정방법으로 최우추정법, 확률가중적률법, 회귀분석법은 기존 연구에서 활발하게 적용되어져 왔다. 그러나 이들 세 가지 추정방법 가운데, 회귀분석법의 우수성은 엄격하게 평가되어진 적이 없다. 본 논문에서는 몬테칼로 시뮬레이션을 통하여 Generalized Extreme Value(GEV) 분포와 Generalized Pareto(GP) 분포의 모수 추정에 회귀분석법 및 다른 추정방법을 적용하여 비교 연구한다. 시뮬레이션 결과, 표본의 크기가 작은 경우 회귀분석 법은 GEV 분포의 위치모수 추정시 편의 측면과 효율성 측면에서 다른 방법보다 우수한 경향을 나타내었다. GP 분포의 규모모수 추정시에는 표본의 크기 가 작을 경우 회귀분석법이 다른 방법보다 작은 편의를 나타내었다. 회귀분석법은 표본의 크기 가 작거나 적당히 큰 경우에도 GEV 분포나 GP 분포의 형태모수 추정시에 형태모수의 값이 -0.4일 경우, 다른 방법보다 우수한 경향을 나타내었다.