• 제목/요약/키워드: distribution free confidence intervals

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Confidence Intervals for the Stress-strength Models with Explanatory Variables

  • Lee, Sangyeol;Park, Eunsik
    • Journal of the Korean Statistical Society
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    • 제27권4호
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    • pp.435-449
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    • 1998
  • In this paper, we consider the problem of constructing the lower cofidence intervals for the reliability P(X < Y z,w), where the stress X and the strength Y are the random variables with explanatory variables z and w, respectively. As an estimator of the reliability, a Mann-Whitney type statistic is considered. It is shown that under regularity conditions, the proposed estimator is asymptotically normal. Based on the result, the distribution free lower confidence intervals are constructed.

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A Comparison of Confidence Intervals for the Reliability of the Stress-Strength Models with Explanatory Variables

  • Eun Sik Park;Jae Joo Kim;Sung Hyun Park
    • Communications for Statistical Applications and Methods
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    • 제3권1호
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    • pp.73-85
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    • 1996
  • In this paper, we consider the distribution-free confidence intervals for the reliability of the stress-strength model when the stress X and strength Y depend linearly on some explanatory variables z and w, respectively. We apply these confidence intervals to the Rocket-Motor data and compare the results to those of Guttman et al. (1988). Some simulation results show that the distribution-free confidence intervals have better performance for nonnormal errors compared to those of Guttman et al. (1988), which are designed for normal random variables in respect that the former yield the coverage levels closer to the nominal coverage level than the latter.

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Nonparametric confidence intervals for quantiles based on a modified ranked set sampling

  • Morabbi, Hakime;Razmkhah, Mostafa;Ahmadi, Jafar
    • Communications for Statistical Applications and Methods
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    • 제23권2호
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    • pp.119-129
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    • 2016
  • A new sampling method is introduced based on the idea of a ranked set sampling scheme in which taken samples in each set are dependent on previous ones. Some theoretical results are presented and distribution-free confidence intervals are derived for the quantiles of any continuous population. It is shown numerically that the proposed sampling scheme may lead to 95% confidence intervals (especially for extreme quantiles) that cannot be found based on the ordinary ranked set sampling scheme presented by Chen (2000) and Balakrishnan and Li (2006). Optimality aspects of this scheme are investigated for both coverage probability and minimum expected length criteria. A real data set is also used to illustrate the proposed procedure. Conclusions are eventually stated.

Estimation of slope , βusing the Sequential Slope in Simple Linear Regression Model

  • Choi, Yong;Kim, Dongjae
    • Communications for Statistical Applications and Methods
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    • 제10권2호
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    • pp.257-266
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    • 2003
  • Distribution-free estimation methods are proposed for slope, $\beta$ in the simple linear regression model. In this paper, we suggest the point estimators using the sequential slope based on sign test and Wilcoxon signed rank test. Also confidence intervals are presented for each estimation methods. Monte Carlo simulation study is carried out to compare the efficiency of these methods with least square method and Theil´s method. Some properties for the proposed methods are discussed.

Bootstrap Estimation for the Process Incapability Index $C_{pp}$

  • Han, Jeong-Hye;Cho, Joong-Jae;Lim, Chun-Sung
    • 한국품질경영학회:학술대회논문집
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    • 한국품질경영학회 1998년도 The 12th Asia Quality Management Symposium* Total Quality Management for Restoring Competitiveness
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    • pp.309-315
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    • 1998
  • Process Capability can be expressed with a process index which indicates the incapability of a process to meet its specifications. This index is regarded as a process capability index(PCI) or more precisely as a process incapability index(PII). It is obtained from a simple transformation of a PCI. Greenwich and Jahr-Schaffrath(1995) considered the PII $C_{pp}$ which could be obtained from the transformation to the PCI, $C_{pm}$, and they provided the asymptotic distribution for $C_{pp}$ which was useful unless the process characteristic was normally distributed. However, some statistical inferences based on the asymptotic distribution need a large sample size. There are some processes which process engineers could not help obtaining sufficiently a large sample size. Thus, we have derived its corresponding bootstrap asymptotic distribution since bootstrapping would be a helpful technique for the PII, $C_{pp}$ which was nonparametric or free from assumptions of the distribution of the characteristic X. Moreover, we have constructed six bootstrap confidence intervals used in reducing bias of estimations based on the bootstrap asymptotic distribution and simulated their performances for $C_{pp}$,

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