• 제목/요약/키워드: bootstrap technique

검색결과 59건 처리시간 0.026초

Estimating the Queue Length Distribution of ATM multiplexer using Threshold Bootstrap

  • 김윤배
    • 한국시뮬레이션학회:학술대회논문집
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    • 한국시뮬레이션학회 1999년도 추계학술대회 논문집
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    • pp.62-62
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    • 1999
  • In this paper, we propose a new technique of estimating tail of steady-state queue length distribution; Pr(Q>q), fo ATM multiplexer. Pr(Q>q) is a fundamental measure of network congestion. Assessing Pr(Q>q) properly is crucial for design and control of ATM networks. Data traffic pattern of high-speed networks is highly correlated and bursty. Estimating Pr(Q>q) is very difficult because of correlation and burstiness. We estimate entropy(rate-function) using large deviation principles and threshold bootstrap. Simulation studies are conducted to compare the performance of an existing method and our new method.

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Developing a Molecular Prognostic Predictor of a Cancer based on a Small Sample

  • Kim Inyoung;Lee Sunho;Rha Sun Young;Kim Byungsoo
    • 한국통계학회:학술대회논문집
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    • 한국통계학회 2004년도 학술발표논문집
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    • pp.195-198
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    • 2004
  • One Important problem in a cancer microarray study is to identify a set of genes from which a molecular prognostic indicator can be developed. In parallel with this problem is to validate the chosen set of genes. We develop in this note a K-fold cross validation procedure by combining a 'pre-validation' technique and a bootstrap resampling procedure in the Cox regression . The pre-validation technique predicts the microarray predictor of a case without having seen the true class level of the case. It was suggested by Tibshirani and Efron (2002) to avoid the possible over-fitting in the regression in which a microarray based predictor is employed. The bootstrap resampling procedure for the Cox regression was proposed by Sauerbrei and Schumacher (1992) as a means of overcoming the instability of a stepwise selection procedure. We apply this K-fold cross validation to the microarray data of 92 gastric cancers of which the experiment was conducted at Cancer Metastasis Research Center, Yonsei University. We also share some of our experience on the 'false positive' result due to the information leak.

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혼합물실험에서 능형회귀추정량에 대한 두 종류의 붓스트랩 신뢰구간 (Two Bootstrap Confidence Intervals of Ridge Regression Estimators in Mixture Experiments)

  • 장대흥
    • 응용통계연구
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    • 제19권2호
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    • pp.339-347
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    • 2006
  • 혼합물실험에서 제한된 영역 때문에 공선성문제가 발생하면 회귀계수에 대한 추정값이 매우 불안정하게 되므로 이를 해결하기 위하여 우리는 주로 능형추정량을 사용한다. 이 때 붓스트랩 기법을 사용하면 능형추정량에 대한 붓스트랩 신뢰구간을 구할 수 있다. 본 논문에서는 제한된 영역을 갖는 혼합물실험의 한 예를 통하여 붓스트랩 잔차 방법과 붓스트랩 쌍 방법 각각에 대하여 능형회귀추정량에 대한 붓스트랩 신뢰구간을 구하고 서로 비교하였다.

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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지렛대 붓스트랩을 이용한 이변량 구간 중도 절단 자료의 일치성 검정 (A concordance test for bivariate interval censored data using a leverage bootstrap)

  • 김양진
    • 응용통계연구
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    • 제32권5호
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    • pp.753-761
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    • 2019
  • 본 논문에서는 이변량 구간 중도 절단 자료의 연관성 검정을 연구하고자 한다. Kendall's τ 통계량은 분포의 가정을 필요로 하지 않는 비모수방법으로 연관성 검정을 위해 빈번히 적용되고 있다. 본 논문에서도 이러한 τ 통계량을 이용한 검정을 하기 위해 붓스트랩 방법을 적용시킨다. 일반적인 비모수 붓스트랩 방법의 구간 중도 절단에 적용은 편의된 결과를 보여주었다. 이는 구간 중도 절단자료의 불완전성(incompleteness)과 관련된 것으로 이를 극복하기 위해 지렛대 붓스트랩 방법을 적용하였다. 추정된 분포에 근거하여 구간 중도 절단 대신 모의 완전한 표본(pseudo complete data)을 추룰하는 것이다. 본 논문에서는 재표본의 크기 m을 결정하기 위해 기존 연구자의 공식을 이용하였다. 시행된 모의 실험의 결과는 바람직한 제 1종 오류값과 좋은 검정력을 보였주었으며 실제 적용 예로 AIDS 자료에서 HIV 감염시점과 바이러스 잠복 시간과의 연관성 여부를 검정해보았다.

Diagnostic Study of Problems under Asymptotically Generalized Least Squares Estimation of Physical Health Model

  • Kim, Jung-Hee
    • 대한간호학회지
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    • 제29권5호
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    • pp.1030-1041
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    • 1999
  • This study examined those problems noticed under the Asymptotically Generalized Least Squares estimator in evaluating a structural model of physical health. The problems were highly correlated parameter estimates and high standard errors of some parameter estimates. Separate analyses of the endogenous part of the model and of the metric of a latent factor revealed a highly skewed and kurtotic measurement indicator as the focal point of the manifested problems. Since the sample sizes are far below that needed to produce adequate AGLS estimates in the given modeling conditions, the adequacy of the Maximum Likelihood estimator is further examined with the robust statistics and the bootstrap method. These methods demonstrated that the ML methods were unbiased and statistical decisions based upon the ML standard errors remained almost the same. Suggestions are made for future studies adopting structural equation modeling technique in terms of selecting of a reference indicator and adopting those statistics corrected for nonormality.

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붓스트랩을 활용한 이상원인변수의 탐지 기법 (Bootstrap-Based Fault Identification Method)

  • 강지훈;김성범
    • 품질경영학회지
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    • 제39권2호
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    • pp.234-243
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    • 2011
  • Multivariate control charts are widely used to monitor the performance of a multivariate process over time to maintain control of the process. Although existing multivariate control charts provide control limits to monitor the process and detect any extraordinary events, it is a challenge to identify the causes of an out-of-control alarm when the number of process variables is large. Several fault identification methods have been developed to address this issue. However, these methods require a normality assumption of the process data. In the present study, we propose a bootstrapped-based $T^2$ decomposition technique that does not require any distributional assumption. A simulation study was conducted to examine the properties of the proposed fault identification method under various scenarios and compare it with the existing parametric $T^2$ decomposition method. The simulation results showed that the proposed method produced better results than the existing one, especially in nonnormal situations.

Application of Bootstrap Method to Primary Model of Microbial Food Quality Change

  • Lee, Dong-Sun;Park, Jin-Pyo
    • Food Science and Biotechnology
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    • 제17권6호
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    • pp.1352-1356
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    • 2008
  • Bootstrap method, a computer-intensive statistical technique to estimate the distribution of a statistic was applied to deal with uncertainty and variability of the experimental data in stochastic prediction modeling of microbial growth on a chill-stored food. Three different bootstrapping methods for the curve-fitting to the microbial count data were compared in determining the parameters of Baranyi and Roberts growth model: nonlinear regression to static version function with resampling residuals onto all the experimental microbial count data; static version regression onto mean counts at sampling times; dynamic version fitting of differential equations onto the bootstrapped mean counts. All the methods outputted almost same mean values of the parameters with difference in their distribution. Parameter search according to the dynamic form of differential equations resulted in the largest distribution of the model parameters but produced the confidence interval of the predicted microbial count close to those of nonlinear regression of static equation.

Bootstrap 기법을 이용한 개의 혈청검사 일부 항목의 참고범위 평가 (Evaluation of Reference Intervals of Some Selected Chemistry Parameters using Bootstrap Technique in Dogs)

  • 김으뜸;박선일
    • 한국임상수의학회지
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    • 제24권4호
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    • pp.509-513
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    • 2007
  • 혈청검사항목의 해석기준으로 사용하는 참고범위는 측정 장비와 병원마다 차이를 보이기 때문에 병원 간 정보를 교환하고 해석하는데 어려움이 많다. 또한 동일한 병원에서도 내원한 환자의 특성을 고려하여 참고범위를 재설정하는 것이 일반모집단의 특성을 제대로 반영한다. 본 연구에서는 강원대학교 수의학부대학 동물병원에서 설정한 혈청화학 검사 항목의 참고범위를 재평가하기 위하여 2005-2006년 동안 본원에 내원한 임상적으로 건강한 개 100두(1-8세, 체중 2.2-5.8 kg)의 혈청검사 일부 항목을 모수 및 비모수적 bootstrap 모의시험으로 분석하였다. 평가항목은 BUN(mg/dl), cholesterol(mg/dl), calcium(mg/dl), aspartate aminotransferase(AST, U/L), alanine aminotransferase(ALT, U/L), alkaline phosphatase(ALP, U/L) 및 total protein(g/dl)으로 Ektachem DT 60 분석기(Johnson & Johnson)로 측정하였다. 칼슘을 제외한 모든 항목이 왜곡이 매우 심한 분포를 보였으며 특히 혈청 효소항목의 outlier는 전체 자료의 5-9%, 기타 항목은 1-2%를 보였다. 각 항목의 분포에 상관없이 모수적 방법에 비하여 비모수적 방법으로 추정한 참고범위가 임상적으로 유용하였으며 추정된 참고범위는 BUN 14.7(7.0-24.2), cholesterol 227.3(120.7-480.8), calcium 10.9(8.1-12.5), AST 25.4(11.8-66.6), ALT 25.5(11.7-68.9), ALP 87.7(31.1-240.8), and total protein 6.8(5.6-8.2)로 나타났다. 이러한 결과는 모집단의 특성을 고려하여 참고범위를 재설정하는데 비모수적 모의시험이 매우 유용하며 특히 측정항목의 분포에 무관하게 사용할 수 있는 장점이 있는 것으로 사료된다.

Kernel Inference on the Inverse Weibull Distribution

  • Maswadah, M.
    • Communications for Statistical Applications and Methods
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    • 제13권3호
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    • pp.503-512
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    • 2006
  • In this paper, the Inverse Weibull distribution parameters have been estimated using a new estimation technique based on the non-parametric kernel density function that introduced as an alternative and reliable technique for estimation in life testing models. This technique will require bootstrapping from a set of sample observations for constructing the density functions of pivotal quantities and thus the confidence intervals for the distribution parameters. The performances of this technique have been studied comparing to the conditional inference on the basis of the mean lengths and the covering percentage of the confidence intervals, via Monte Carlo simulations. The simulation results indicated the robustness of the proposed method that yield reasonably accurate inferences even with fewer bootstrap replications and it is easy to be used than the conditional approach. Finally, a numerical example is given to illustrate the densities and the inferential methods developed in this paper.