• 제목/요약/키워드: Distribution EVD

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근사 꼬리분포의 유형별 적용 모형 고찰 (Review of Application Models According to the Classification of Asymptotic Tail Distribution)

  • 최성운
    • 대한안전경영과학회:학술대회논문집
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    • 대한안전경영과학회 2010년도 추계학술대회
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    • pp.35-39
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    • 2010
  • The research classifies three types of asymptotic tail distributions such as long(heavy, thick) tailed distribution, medium tailed distribution and short(light, thin) tailed distribution. The extreme value distributions(EVD) classified in this paper can be used in SPC(Statistical Process Control) control chart and reliability engineering.

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실무적 적용 관점에서 신뢰성 분포의 유형화 모형의 고찰 (Review of Classification Models for Reliability Distributions from the Perspective of Practical Implementation)

  • 최성운
    • 대한안전경영과학회지
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    • 제13권1호
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    • pp.195-202
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    • 2011
  • The study interprets each of three classification models based on Bath-Tub Failure Rate (BTFR), Extreme Value Distribution (EVD) and Conjugate Bayesian Distribution (CBD). The classification model based on BTFR is analyzed by three failure patterns of decreasing, constant, or increasing which utilize systematic management strategies for reliability of time. Distribution model based on BTFR is identified using individual factors for each of three corresponding cases. First, in case of using shape parameter, the distribution based on BTFR is analyzed with a factor of component or part number. In case of using scale parameter, the distribution model based on BTFR is analyzed with a factor of time precision. Meanwhile, in case of using location parameter, the distribution model based on BTFR is analyzed with a factor of guarantee time. The classification model based on EVD is assorted into long-tailed distribution, medium-tailed distribution, and short-tailed distribution by the length of right-tail in distribution, and depended on asymptotic reliability property which signifies skewness and kurtosis of distribution curve. Furthermore, the classification model based on CBD is relied upon conjugate distribution relations between prior function, likelihood function and posterior function for dimension reduction and easy tractability under the occasion of Bayesian posterior updating.