• 제목/요약/키워드: resampling simulation

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군집의 크기가 생존시간에 영향을 미치는 군집 구간중도절단된 자료에 대한 준모수적 모형 (Modeling Clustered Interval-Censored Failure Time Data with Informative Cluster Size)

  • 김진흠;김윤남
    • 응용통계연구
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    • 제27권2호
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    • pp.331-343
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    • 2014
  • 본 논문에서는 군집 구간중도절단된 자료에서 생존시간이 군집의 크기에 의존할 때 주변모형으로부터 가중 추정 방법과 군집 내 재추출 방법을 써서 모수를 추정하고 그 추정량의 점근적 성질을 살펴보았다. 모의실험을 통해 추정량의 편향의 크기와 신뢰구간의 포함율 측면에서 볼 때 제안한 두 추정 방법이 생존시간과 군집의 크기 간의 종속 관계를 무시한 방법보다 우수한 것으로 나타났다. 제안한 추정 방법을 림프성 사상충 자료에 적용한 결과에 따르면 서로 다른 두 치료방법이 유의하게 다르지 않았으며 나이 효과도 매우 유의하지 않은 것으로 나타났다.

신호의 리샘플링에 의한 실시간 주파수 계측 알고리즘 (A real-time frequency measuring algorithm by resampling of a signal)

  • 윤재현;이승주;김기영;이현철;윤양웅;박형준
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2002년도 하계학술대회 논문집 D
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    • pp.2718-2720
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    • 2002
  • The algorithm that can estimate frequency in real-time by using the resampling of a signal which was known frequency band like biological signals, was suggested in this study. A sinusoid signal is simulated as a practical measured signal. The sinusoid signal is sampled by using the impulse-train, and is subtracted the backward sample from forward by the sampled signals. The continuous sign, such as positive, negative or zero is counted from the calculation result of the subtraction, and those is stored. Therefore, the measured frequency is estimated by using the magnitude of continuous sign and the sampling period. The algorithm designed in this study is proven through the computer simulation.

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Fault Diagnosis Method Based on High Precision CRPF under Complex Noise Environment

  • Wang, Jinhua;Cao, Jie
    • Journal of Information Processing Systems
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    • 제16권3호
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    • pp.530-540
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    • 2020
  • In order to solve the problem of low tracking accuracy caused by complex noise in the fault diagnosis of complex nonlinear system, a fault diagnosis method of high precision cost reference particle filter (CRPF) is proposed. By optimizing the low confidence particles to replace the resampling process, this paper improved the problem of sample impoverishment caused by the sample updating based on risk and cost of CRPF algorithm. This paper attempts to improve the accuracy of state estimation from the essential level of obtaining samples. Then, we study the correlation between the current observation value and the prior state. By adjusting the density variance of state transitions adaptively, the adaptive ability of the algorithm to the complex noises can be enhanced, which is expected to improve the accuracy of fault state tracking. Through the simulation analysis of a fuel unit fault diagnosis, the results show that the accuracy of the algorithm has been improved obviously under the background of complex noise.

On the Equality of Two Distributions Based on Nonparametric Kernel Density Estimator

  • Kim, Dae-Hak;Oh, Kwang-Sik
    • Journal of the Korean Data and Information Science Society
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    • 제14권2호
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    • pp.247-255
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    • 2003
  • Hypothesis testing for the equality of two distributions were considered. Nonparametric kernel density estimates were used for testing equality of distributions. Cross-validatory choice of bandwidth was used in the kernel density estimation. Sampling distribution of considered test statistic were developed by resampling method, called the bootstrap. Small sample Monte Carlo simulation were conducted. Empirical power of considered tests were compared for variety distributions.

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인터넷 환경에서 붓스트랩 통계 시스템의 개발 (Development of Web-based Statistical System for Bootstrap on the Internet Environment)

  • 최성운;임인섭
    • 대한안전경영과학회지
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    • 제6권2호
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    • pp.241-250
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    • 2004
  • Recently, growth of internet causes rapid changes in many areas of statistics such as statistical computation and education. Especially, bootstrap is the most interesting statistical methods applying computer resampling simulation. In this study, we try to present how to use a method of bootstrap on the internet. We also develop to user a statistical system which is programed with java applet for user to handle easily.

인터넷 환경에서 붓스트랩 품질 및 신뢰성 시스템의 개발 (Development of Web-based Quality & Reliability System for Bootstrap on the Internet Environment)

  • 최성운;임인섭
    • 대한안전경영과학회지
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    • 제7권1호
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    • pp.147-157
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    • 2005
  • Recently, growth of internet causes rapid changes in many areas of statistics such as statistical computation and analysis. Especially, bootstrap is the most interesting statistical methods applying computer resampling simulation. In this paper, we try to present how to use a method of bootstrap on the internet. We also develop to user a statistical system which is programed with ASP for user to handle easily in manufacturing system.

Bootstrap Confidence Intervals for the Difference of Quantiles of Right Censored Data

  • Na, Jong-Hwa;Park, Hyo-Il;Jang, Young-Mi
    • Communications for Statistical Applications and Methods
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    • 제11권3호
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    • pp.447-454
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    • 2004
  • In this paper, we consider the bootstrap method to the interval estimation of the difference of quantiles of right censored data. We showed the validity of bootstrap method and compare with others with real data example. In simulation various resampling schemes for right censored data are also considered.

추세 시계열 자료의 부트스트랩 적용 (Applying Bootstrap to Time Series Data Having Trend)

  • 박진수;김윤배;송기범
    • 한국경영과학회지
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    • 제38권2호
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    • pp.65-73
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    • 2013
  • In the simulation output analysis, bootstrap method is an applicable resampling technique to insufficient data which are not significant statistically. The moving block bootstrap, the stationary bootstrap, and the threshold bootstrap are typical bootstrap methods to be used for autocorrelated time series data. They are nonparametric methods for stationary time series data, which correctly describe the original data. In the simulation output analysis, however, we may not use them because of the non-stationarity in the data set caused by the trend such as increasing or decreasing. In these cases, we can get rid of the trend by differencing the data, which guarantees the stationarity. We can get the bootstrapped data from the differenced stationary data. Taking a reverse transform to the bootstrapped data, finally, we get the pseudo-samples for the original data. In this paper, we introduce the applicability of bootstrap methods to the time series data having trend, and then verify it through the statistical analyses.

붓스크랩 기법을 이용한 다심 광커넥터 손실특성 예측 (Bootstrap Simulation for Performance Evaluation of Optical Multifiber Connectors)

  • 전오곤;강기훈
    • 품질경영학회지
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    • 제26권4호
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    • pp.250-264
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    • 1998
  • The purpose of the thesis is to develop simulation program for forecasting of optical connector. So we can achieve the time and the money saving for making the optical connector. Optical performance (insertion loss) of optical connector mainly relies on 3 misalignment factors-ferrule factor due to mis-manufacture from design, auto-centering effect that is fiber behavior phenomena between hole and fiber, fiber misalignment factor. Simulation use experimental data with auto-centering effect and fiber factor and use pseudo data with ferrule through random number generation because it is developing stage. In this study we a, pp.y kernel density estimation method with experimental data in order to know whether it belong to or not specific parametric distribution family. And we simulate to forecast insertion loss of optical multifiber connector under specific design model using nonparametric bootstrap resampling data and parametric pseudo samples from uniform distribution. We obtain the tolerance specifications of misalignment factors satisfying not exceed in maximum 1.0dB and choose optimal hole diameter.

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