• Title/Summary/Keyword: 모수 추정법

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A Study on the Lifetime Prediction of Device by the Method of Bayesian Estimate (베이지안 추정법에 의한 소자의 수명 예측에 관한 연구)

  • 오종환;오영환
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.19 no.8
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    • pp.1446-1452
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    • 1994
  • In this paper, Weibull distribution is applied to the lifetme distribution of a device. The method of Bayesian estimate used to estimate requiring parameter in order to predict lifetime of device using accelerated lifetime test data, namely failure time of device. The method of Bayesian estimate needs prior information in order to estimate parameter. But this paper proposed the method of parameter estimate without prior information. As stress is temperature, Arrhenius model is applied and the method of linear estimate is applied to predict lifetime of device at the state of normal operation.

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Testing the Existence of a Discontinuity Point in the Variance Function

  • Huh, Jib
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.3
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    • pp.707-716
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    • 2006
  • When the regression function is discontinuous at a point, the variance function is usually discontinuous at the point. In this case, we had better propose a test for the existence of a discontinuity point with the regression function rather than the variance function. In this paper we consider that the variance function only has a discontinuity point. We propose a nonparametric test for the existence of a discontinuity point with the second moment function since the variance function and the second moment function have the same location and jump size of the discontinuity point. The proposed method is based on the asymptotic distribution of the estimated jump size.

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On Practical Choice of Smoothing Parameter in Nonparametric Classification (베이즈 리스크를 이용한 커널형 분류에서 평활모수의 선택)

  • Kim, Rae-Sang;Kang, Kee-Hoon
    • Communications for Statistical Applications and Methods
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    • v.15 no.2
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    • pp.283-292
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    • 2008
  • Smoothing parameter or bandwidth plays a key role in nonparametric classification based on kernel density estimation. We consider choosing smoothing parameter in nonparametric classification, which optimize the Bayes risk. Hall and Kang (2005) clarified the theoretical properties of smoothing parameter in terms of minimizing Bayes risk and derived the optimal order of it. Bootstrap method was used in their exploring numerical properties. We compare cross-validation and bootstrap method numerically in terms of optimal order of bandwidth. Effects on misclassification rate are also examined. We confirm that bootstrap method is superior to cross-validation in both cases.

Comparison of estimation methods for expectile regression (평률 회귀분석을 위한 추정 방법의 비교)

  • Kim, Jong Min;Kang, Kee-Hoon
    • The Korean Journal of Applied Statistics
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    • v.31 no.3
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    • pp.343-352
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    • 2018
  • We can use quantile regression and expectile regression analysis to estimate trends in extreme regions as well as the average trends of response variables in given explanatory variables. In this paper, we compare the performance between the parametric and nonparametric methods for expectile regression. We introduce each estimation method and analyze through various simulations and the application to real data. The nonparametric model showed better results if the model is complex and difficult to deduce the relationship between variables. The use of nonparametric methods can be recommended in terms of the difficulty of assuming a parametric model in expectile regression.

A comparison on coefficient estimation methods in single index models (단일지표모형에서 계수 추정방법의 비교)

  • Choi, Young-Woong;Kang, Kee-Hoon
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.6
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    • pp.1171-1180
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    • 2010
  • It is well known that the asymptotic convergence rates of nonparametric regression estimator gets worse as the dimension of covariates gets larger. One possible way to overcome this problem is reducing the dimension of covariates by using single index models. Two coefficient estimation methods in single index models are introduced. One is semiparametric least square estimation method, which tries to find approximate solution by using iterative computation. The other one is weighted average derivative estimation method, which is non-iterative method. Both of these methods offer the parametric convergence rate to normal distribution. However, practical comparison of these two methods has not been done yet. In this article, we compare these methods by examining the variances of estimators in various models.

결측치를 가진 목표지향형 평가모델에서 수학학습능력의 평가에 관한 연구

  • 홍석강
    • The Mathematical Education
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    • v.33 no.2
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    • pp.167-175
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    • 1994
  • 결측치를 가진 회귀모형의 모수 추정법을 이용하여 목표지향형 평가 모델에서 기초고사(X)와 신고사(I)(Y), 신고사(II)(Z)등 두 개 이상의 고사지로 이루어진 고사집에서 기초고사에는 결측치가 없고 신고사(I), 신고사(II)등에는 결측치가 있는 경우 모수의 최우추정량 계산법을 논하고 E.M. 알고리즘과 평가치는 희귀방적식화에 의하여 우리나라 중등학교 학생의 수학학습능력과 수학적 사고력의 크기를 변별하며 학생들의 진능력이 반영된 평가모델과 최종 성적을 평가 할 수 있는 계산법을 제시하였다.

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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.

Shrinkage Small Area Estimation Using a Semiparametric Mixed Model (준모수혼합모형을 이용한 축소소지역추정)

  • Jeong, Seok-Oh;Choo, Manho;Shin, Key-Il
    • The Korean Journal of Applied Statistics
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    • v.27 no.4
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    • pp.605-617
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    • 2014
  • Small area estimation is a statistical inference method to overcome large variance due to a small sample size allocated in a small area. A shrinkage estimator obtained by minimizing relative error(RE) instead of MSE has been suggested. The estimator takes advantage of good interpretation when the data range is large. A semiparametric estimator is also studied for small area estimation. In this study, we suggest a semiparametric shrinkage small area estimator and compare small area estimators using labor statistics.

Prediction Intervals for Nonlinear Time Series Models Using the Bootstrap Method (붓스트랩을 이용한 비선형 시계열 모형의 예측구간)

  • 이성덕;김주성
    • The Korean Journal of Applied Statistics
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    • v.17 no.2
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    • pp.219-228
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    • 2004
  • In this paper we construct prediction intervals for nonlinear time series models using the bootstrap. We compare these prediction intervals to traditional asymptotic prediction intervals using quasi-score estimation function and M-quasi-score estimating function comprising bounded functions. Simulation results show that the bootstrap method leads to improved accuracy. The accuracy of the bootstrap is empirically demonstrated with the consumer price index.

선물의 수익률과 변동성에 대한 장기기억 효과 분석

  • Lee, Jeong-Hyeong
    • 한국데이터정보과학회:학술대회논문집
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    • 2004.04a
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    • pp.103-110
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    • 2004
  • 본 논문에서 한국선물시장의 변동성과 수익률에 대한 장기기억의 경험적 근거를 보이기 위해 일별 수익률과 변동성에 대하여 장기기억성의 추정과 검정을 실시하였다. Geweke and Porter-Hudak(1983)의 반비모수적 추정법을 이용하여 장기기억모수를 추정하였으며 추정결과 수익률은 장기기억효과가 없었으며, 변동성에서 장기기억효과가 유의한 것으로 나타났다.

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