• Title/Summary/Keyword: Empirical Data

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Empirical Bayes Confidence Intervals of the Burr Type XII Failure Model

  • Choi, Dal-Woo
    • Journal of the Korean Data and Information Science Society
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    • v.10 no.1
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    • pp.155-162
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    • 1999
  • This paper is concerned with the empirical Bayes estimation of one of the two shape parameters(${\theta}$) in the Burr(${\beta},\;{\theta}$) type XII failure model based on type-II censored data. We obtain the bootstrap empirical Bayes confidence intervals of ${\theta}$ by the parametric bootstrap introduced by Laird and Louis(1987). The comparisons among the bootstrap and the naive empirical Bayes confidence intervals through Monte Carlo study are also presented.

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Empirical modelling approaches to modelling failures

  • Baik, Jaiwook;Jo, Jinnam
    • International Journal of Reliability and Applications
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    • v.14 no.2
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    • pp.107-114
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    • 2013
  • Modelling of failures is an important element of reliability modelling. Empirical modelling approach suitable for complex item is explored in this paper. First step of the empirical modelling approach is to plot hazard function, density function, Weibull probability plot as well as cumulative intensity function to see which model fits best for the given data. Next step of the empirical modelling approach is select appropriate model for the data and fit the parametric model accordingly and estimate the parameters.

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R&D Investments and Ownership Structure (R&D 투자와 소유구조)

  • Cho Shin;Yoon Choong-Han
    • Journal of Korea Technology Innovation Society
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    • v.8 no.3
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    • pp.1199-1224
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    • 2005
  • This study analyzes various factors affecting a firm's investments, focusing on both a firm's ownership structure and CEO's incentives. While previous empirical works focus on various financial data in order to test the Schumpeterian Hypotheses, this paper is using various ownership structure data as well as financial data. Empirical results show that the greater a firm's CEO has the company's own stock, the less he spends in R&D investments. The main empirical results of this study is in line with past empirical studies on various markets outside of Korea.

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Parametric Empirical Bayes Estimation of A Constant Hazard with Right Censored Data

  • Mashayekhi, Mostafa
    • International Journal of Reliability and Applications
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    • v.2 no.1
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    • pp.49-56
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    • 2001
  • In this paper we consider empirical Bayes estimation of the hazard rate and survival probabilities with right censored data under the assumption that the hazard function is constant over the period of observation and the prior distribution is gamma. We provide an estimator of the first derivative of the prior moment generating function that converges at each point to the true value in $L_2$ and use it to obtain, easy to compute, asymptotically optimal estimators under the squared error loss function.

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Parametric Empirical Bayes Estimators with Item-Censored Data

  • Choi, Dal-Woo
    • Journal of the Korean Data and Information Science Society
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    • v.8 no.2
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    • pp.261-270
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    • 1997
  • This paper is proposed the parametric empirical Bayes(EB) confidence intervals which corrects the deficiencies in the naive EB confidence intervals of the scale parameter in the Weibull distribution under item-censoring scheme. In this case, the bootstrap EB confidence intervals are obtained by the parametric bootstrap introduced by Laird and Louis(1987). The comparisons among the bootstrap and the naive EB confidence intervals through Monte Carlo study are also presented.

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A Comparison on the Empirical Power of Some Normality Tests

  • Kim, Dae-Hak;Eom, Jun-Hyeok;Jeong, Heong-Chul
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.1
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    • pp.31-39
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    • 2006
  • In many cases, we frequently get a desired information based on the appropriate statistical analysis of collected data sets. Lots of statistical theory rely on the assumption of the normality of the data. In this paper, we compare the empirical power of some normality tests including sample entropy quantity. Monte carlo simulation is conducted for the calculation of empirical power of considered normality tests by varying sample sizes for various distributions.

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A Comparative Study for Several Bayesian Estimators Under Balanced Loss Function

  • Kim, Yeong-Hwa
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.2
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    • pp.291-300
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    • 2006
  • In this research, the performance of widely used Bayesian estimators such as Bayes estimator, empirical Bayes estimator, constrained Bayes estimator and constrained empirical Bayes estimator are compared by means of a measurement under balanced loss function for the typical normal-normal situation. The proposed measurement is a weighted sum of the precisions of first and second moments. As a result, one can gets the criterion according to the size of prior variance against the population variance.

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An Empirical Study on Jitter between Two Servers Port Connections

  • Lee, Sang-Bock;Kim, Hyun-Soo
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.1
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    • pp.87-94
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    • 2006
  • The purpose of this paper is to measure jitter between two server systems. Given 3 empirical models as user and port parallel server types under some conditions of 100 Mbps and optimal CPU temperature suggested by Lee and Kim(2005), our results are shown; jitter was usually measured above 3000 ms in most empirical cases, jumping points were observed around 250 processing traffics, and port parallel model was optimal in our cases.

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Variogram Estimation of Tropospheric Delay by Using Meteorological Data

  • Kim, Bu-Gyeom;Kim, Jong-Heon;Kee, Changdon;Kim, Donguk
    • Journal of Positioning, Navigation, and Timing
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    • v.10 no.4
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    • pp.271-278
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    • 2021
  • In this paper, a tropospheric delay error was calculated by using meteorological data collect from weather station and Saastamoinen model, and an empirical variogram of the tropospheric delay in the Korean peninsula was estimated. In order to estimate the empirical variogram of the tropospheric delay according to weather condition, sunny day, rainy day, and typhoon day were selected as analysis days. Analysis results show that a maximum correlation range of the empirical variogram on sunny day was about 560 km because there is overall trend of the tropospheric delay. On the other hand, the maximum correlation range of the empirical variogram on rainy was about 150 km because the regional variation was large. Although there is regional variation when the typhoon exists, there is a trend of the tropospheric delay due to a movement of the typhoon. Therefore, the maximum correlation range of the empirical variogram on typhoon day was about 280 km which is between sunny and rainy day.

Empirical Bayes Inferences in the Burr Distribution by the Bootstrap Methods

  • Cho, Kil-Ho;Cho, Jang-Sik;Jeong, Seong-Hwa;Shin, Jae-Seock
    • Journal of the Korean Data and Information Science Society
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    • v.15 no.3
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    • pp.625-632
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    • 2004
  • We consider the empirical Bayes confidence intervals that attain a specified level of EB coverage for the scale parameter in the Burr distribution under type II censoring data. Also, we compare the coverage probabilities and the expected confidence interval lengths for these confidence intervals through simulation study.

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