• Title/Summary/Keyword: 모수 방법

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지수분포 모수함수 간의 다중비교에 관한 연구

  • Kim, Dae-Hwang;Kim, Hye-Jung
    • Proceedings of the Korean Statistical Society Conference
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    • 2003.10a
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    • pp.239-244
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    • 2003
  • 본 연구에서는 확률모형의 모수로부터 얻어지는 여러형태의 함수간의 크기를 다중비교 하는 방법을 제안하고자 한다. 이 방법은 비교대상인 모수 함수간의 선호확률을 베이지안 방법으로 추정하고, 이들로부터 얻어지는 선호행렬을 이용한 새로운 다중비교법이다. 이러한 방법의 제안에 필요한 이론과 비교기준을 고안하였으며, 응용 예로, 제안된 방법을 s개의 독립인 지수분포 모수의 기하평균 크기비교에 적용하였다.

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Semi-parametric Bootstrap Confidence Intervals for High-Quantiles of Heavy-Tailed Distributions (꼬리가 두꺼운 분포의 고분위수에 대한 준모수적 붓스트랩 신뢰구간)

  • Kim, Ji-Hyun
    • Communications for Statistical Applications and Methods
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    • v.18 no.6
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    • pp.717-732
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    • 2011
  • We consider bootstrap confidence intervals for high quantiles of heavy-tailed distribution. A semi-parametric method is compared with the non-parametric and the parametric method through simulation study.

Bayesian Estimation of k-Population Weibull Distribution Under Ordered Scale Parameters (순서를 갖는 척도모수들의 사전정보 하에 k-모집단 와이블분포의 베이지안 모수추정)

  • 손영숙;김성욱
    • The Korean Journal of Applied Statistics
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    • v.16 no.2
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    • pp.273-282
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    • 2003
  • The problem of estimating the parameters of k-population Weibull distributions is discussed under the prior of ordered scale parameters. Parameters are estimated by the Gibbs sampling method. Since the conditional posterior distribution of the shape parameter in the Gibbs sampler is not log-concave, the shape parameter is generated by the adaptive rejection sampling. Finally, we applied this estimation methodology to the data discussed in Nelson (1970).

Parametric nonparametric methods for estimating extreme value distribution (극단값 분포 추정을 위한 모수적 비모수적 방법)

  • Woo, Seunghyun;Kang, Kee-Hoon
    • The Journal of the Convergence on Culture Technology
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    • v.8 no.1
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    • pp.531-536
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    • 2022
  • This paper compared the performance of the parametric method and the nonparametric method when estimating the distribution for the tail of the distribution with heavy tails. For the parametric method, the generalized extreme value distribution and the generalized Pareto distribution were used, and for the nonparametric method, the kernel density estimation method was applied. For comparison of the two approaches, the results of function estimation by applying the block maximum value model and the threshold excess model using daily fine dust public data for each observatory in Seoul from 2014 to 2018 are shown together. In addition, the area where high concentrations of fine dust will occur was predicted through the return level.

깁스표본기법을 이용한 와이블분포의 모수추정

  • 이우동;이창순;강상길
    • Journal of Korea Society of Industrial Information Systems
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    • v.3 no.1
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    • pp.13-21
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    • 1998
  • 와이블분포의 척도모수와 형상모수를 베이지안 방법을 이용하여 추정한다. 깁스표본법을 사용하여 모수들에 대한 추정, 결합사후확률분포와 주변사후확률분포를 구한다. 9개의 열 전달기기자료와 10개의 인위적인 자료를 이용하여 제안된 방법을 적용하여 사례를 연구한다.

An Estimation of Parameters in Weibull Distribution using Gibbs Sampler (깁스표본기법을 이용한 와이블분포의 모수추정)

  • 이우동;이창순;강상길
    • Proceedings of the Korea Society for Industrial Systems Conference
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    • 1997.11a
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    • pp.521-533
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    • 1997
  • 와이블분포에서 척도모수와 형상모수를 베이지안 방법을 이용하여 추정한다. 깁스표본법을 사용하여 모수들에 대한 추정, 결합사후확률분포 와 주변사후확률분포를 구한다. 9개의 열 전달기기자료와 10개의 인위적인 자료를 이용하여 제안된 방법을 적용하여 사례를 연구한다.

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The Development of an easy a simple of Parameter Estimation Method for Reliability Evaluation of Application Software System (응용 소프트웨어 시스템의 신뢰성 평가를 위한 간편한 모수추정방법 개발)

  • Kim, Suk-Hee;Kim, Jong-Hun
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.11 no.2
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    • pp.540-549
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    • 2010
  • The existing reliability evaluation models which have already developed by the corporations are so various because of using Maximum Likelihood Method. The existing models are very complicated owing to using system designing methods. Therefore, it is very difficult to utilize the existing models in business fields of many corporations. The purposes of this paper are as follows: The first purpose is to study the simple estimated Parameter to be easily utilized in the business fields of the corporations. The second purpose is to testify the simplification of the developed Parameter of estimated method by comparing the developed reliability evaluation model with the existing reliability evaluation models which are used in the business fields of the corporations.

A comparison and prediction of total fertility rate using parametric, non-parametric, and Bayesian model (모수, 비모수, 베이지안 출산율 모형을 활용한 합계출산율 예측과 비교)

  • Oh, Jinho
    • The Korean Journal of Applied Statistics
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    • v.31 no.6
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    • pp.677-692
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    • 2018
  • The total fertility rate of Korea was 1.05 in 2017, showing a return to the 1.08 level in the year 2005. 1.05 is a very low fertility level that is far from replacement level fertility or safety zone 1.5. The number may indicate a low fertility trap. It is therefore important to predict fertility than at any other time. In the meantime, we have predicted the age-specific fertility rate and total fertility rate by various statistical methods. When the data trend is disconnected or fluctuating, it applied a nonparametric method applying the smoothness and weight. In addition, the Bayesian method of using the pre-distribution of fertility rates in advanced countries with reference to the three-stage transition phenomenon have been applied. This paper examines which method is reasonable in terms of precision and feasibility by applying estimation, forecasting, and comparing the results of the recent variability of the Korean fertility rate with parametric, non-parametric and Bayesian methods. The results of the analysis showed that the total fertility rate was in the order of KOSTAT's total fertility rate, Bayesian, parametric and non-parametric method outcomes. Given the level of TFR 1.05 in 2017, the predicted total fertility rate derived from the parametric and nonparametric models is most reasonable. In addition, if a fertility rate data is highly complete and a quality is good, the parametric model approach is superior to other methods in terms of parameter estimation, calculation efficiency and goodness-of-fit.

Nonparametric procedures using aligned method and joint placement in randomized block design (랜덤화 블록 계획법에서 정렬방법과 결합 위치를 이용한 비모수 검정법)

  • Jo, Sungdong;Kim, Dongjae
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.1
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    • pp.95-103
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    • 2013
  • Nonparametric procedure in randomized block design (RBD) was proposed by Friedman (1937) for general alternatives. Also Page (1963) suggested the test for ordered alternatives in RBD. In this paper, we proposed the new nonparametric method in randomized block design using aligned method suggested by Hodges and Lehmann (1962) and the joint placement described in Chung and Kim (2007). Also, Monte Carlo simulation study was adapted to compare the power of the proposed procedure with those of previous procedure.

Nonparametric Method Using an Alignment Method in a Randomized Block Design with Replications (반복이 있는 랜덤화 블록 계획법에서 정렬 방법을 이용한 비모수 검정법)

  • Lee, Min-Hee;Kim, Dong-Jae
    • Communications for Statistical Applications and Methods
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    • v.19 no.1
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    • pp.77-84
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    • 2012
  • Mack and Skillings (1980) proposed a typical nonparametric method in a randomized block design with replications. However, this method may lose information because of the use of average observations instead of individual observations. In this paper, we proposed a nonparametric method that employed an aligned method suggested by Hodges and Lehmann (1962) under a randomized block design with replications. In addition, the comparative results of a Monte Carlo power study are presented.