• Title/Summary/Keyword: Order Statistics

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A Note on Bayesian Prediction Analysis for the Rayleigh Model in the presence of Outliers

  • Ko, Jeong-Hwan;Kim, Yeung-Hoon
    • 한국데이터정보과학회:학술대회논문집
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    • 2003.05a
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    • pp.171-176
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    • 2003
  • This paper deals with the problem of predicting order statistics in samples from a Rayleigh population when an outlier is present. Bayesian predictive distribution and prediction bounds of the p-th order statistics is obtained where an outlier of type $\theta\delta$ is present. In this connection, some identies are derived.

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NONINFORMATIVE PRIORS FOR PARETO DISTRIBUTION : REGULAR CASE

  • Kim, Dal-Ho;Lee, Woo-Dong;Kang, Sang-Gil
    • 한국데이터정보과학회:학술대회논문집
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    • 2003.05a
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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.

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OBJECTIVE BAYESIAN APPROACH TO STEP STRESS ACCELERATED LIFE TESTS

  • Kim Dal-Ho;Lee Woo-Dong;Kang Sang-Gil
    • Journal of the Korean Statistical Society
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    • v.35 no.3
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    • pp.225-238
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    • 2006
  • This paper considers noninformative priors for the scale parameter of exponential distribution when the data are collected in step stress accelerated life tests. We find the Jeffreys' and reference priors for this model and show that the reference prior satisfies first order matching criterion. Also, we show that there exists no second order matching prior in this problem. Some simulation results are given and we perform Bayesian analysis for proposed priors using some data.

Developing Noninformative Priors for Parallel-Line Bioassay

  • Kim, YeongHwa;Heo, JungEun
    • Communications for Statistical Applications and Methods
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    • v.9 no.2
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    • pp.401-410
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    • 2002
  • This paper revisits parallel-line bioassay problem, from a Bayesian point of view using noninformative priors such as Jeffreys' prior, reference priors, and probability matching priors. After finding the orthogonal transformation, the class of first order and second order probability matching priors are derived. Jeffreys' prior and reference priors are derived also. Numerical examples are given to show the effectiveness of noninformative priors.

A New Test for New Better than Used Class (NBU에 대한 지수성 검정법에 관한 연구)

  • Kim, Hwan-Joong
    • Journal of Korean Society for Quality Management
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    • v.27 no.2
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    • pp.143-151
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    • 1999
  • In this thesis, a new test statistic is proposed for testing exponentiality against New Better than Used (NBU) alternatives. Our test statistic, which is based on a quadratic function of the order statistics from the sample, is readily applied in the case of small sample. Also, Our test statistic is more simple than the test statistic of Hollander and Proschan(1972).

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On the Performance of Iterated Wild Bootstrap Interval Estimation of the Mean Response

  • Kim, Woo-Chul;Ko, Duk-Hyun
    • Journal of the Korean Statistical Society
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    • v.24 no.2
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    • pp.551-562
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    • 1995
  • We consider the iterated bootstrap method in regression model with heterogeneous error variances. The iterated wild bootstrap confidence intervla of the mean response is considered. It is shown that the iterated wild bootstrap confidence interval has coverage error of order $n^{-1}$ wheresa percentile method interval has an error of order $n^{-1/2}$. The simulation results reveal that the iterated bootstrap method calibrates the coverage error of percentile method interval successfully even for the small sample size.

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Testing Uniformity Based on Regression and EDF

  • Kim, Nam-Hyun
    • Communications for Statistical Applications and Methods
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    • v.14 no.3
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    • pp.623-632
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    • 2007
  • Some tests of the goodness of fit of the uniform distribution between 0 and 1 are presented. The powers of the tests under certain alternatives are examined. As a result, the statistic based on the difference between the order statistics and the modal value of them gives good powers. We also give modifications of the statistic without using the extensive tables of the critical points.

Performance analysis of CFAR detectors based on order statistics for nonhomogeneous background (비균일 환경에서 표적 검파를 위한 순서계통에 근거한 일정오경보율 검파기의 성능 해석)

  • 한동석
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.22 no.7
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    • pp.1550-1558
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    • 1997
  • In this paper, we first propose a modified OS CFAR detector called the order statistics cell averaging(OSCA) CFAR detector and anlyze its performance for a Rayleigh target in homogeneous backgrounds, clutter edges, and satistics smallest of(OSSO) CFAR detectors for a Rayleigh target to nonhomogeneous environments. Computer simulation results show that the OSCA CFAR detector has superior performance to OS, OSGO, and OSSO CFAR detectors in homogeneous and multiple target environments. And the proposed detector shows its robustness for fast detection because it requires falf the processing time of the OS CFAR detector.

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SOME MAJORIZATION PROBLEMS ASSOCIATED WITH p-VALENTLY STARLIKE AND CONVEX FUNCTIONS OF COMPLEX ORDER

  • Altintas, Osman;Srivastava, H.M.
    • East Asian mathematical journal
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    • v.17 no.2
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    • pp.175-183
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    • 2001
  • The main object of this paper is to investigate several majorization problems involving two subclasses $S_{p,q}(\gamma)$ and $C_{p,q}(\gamma)$ of p-valently starlike and p-valently convex functions of complex order ${\gamma}{\neq}0$ in the open unit disk $\mathbb{u}$. Relevant connections of the results presented here with those given by earlier workers on the subject are also indicated.

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Bayesian Prediction Inference for Censored Pareto Model

  • Ko, Jeong-Hwan;Kim, Young-Hoon
    • Journal of the Korean Data and Information Science Society
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    • v.10 no.1
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    • pp.147-154
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    • 1999
  • Using a noninformative prior and an inverted gamma prior, the Bayesian predictive density and the prediction intervals for a future observation or the p - th order statistic of n' future observations from the censord Pareto model have been obtained. In additions, numerical examples are given in order to illustrate the proposed predictive procedure.

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