• 제목/요약/키워드: Confidence Interval

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Interval Estimation for Sum of Variance Components in a Simple Linear Regression Model with Unbalanced Nested Error Structure

  • Park, Dong-Joon
    • Communications for Statistical Applications and Methods
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    • 제10권2호
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    • pp.361-370
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    • 2003
  • Those who are interested in making inferences concerning linear combination of valiance components in a simple linear regression model with unbalanced nested error structure can use the confidence intervals proposed in this paper. Two approximate confidence intervals for the sum of two variance components in the model are proposed. Simulation study is peformed to compare the methods. The methods are applied to a numerical example and recommendations are given for choosing a proper interval.

Confidence Interval for the Variance Component in a Unbalanced One-way Random Effects Model

  • 송규문
    • Journal of the Korean Data and Information Science Society
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    • 제13권2호
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    • pp.329-340
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    • 2002
  • Two methods are proposed for constructing a confidence interval on the among group variance component in a unbalanced one-way random effects model. Computer simulation is used to compare these methods with alternative procedures. The results indicate that the method1 and methods2 perform well over small group size and large sample size respectively.

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An Empirical Comparison of Ratio and PPS Strategies

  • Sahoo, L.N.;Dalabehera, M.
    • Journal of the Korean Statistical Society
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    • 제31권2호
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    • pp.143-152
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    • 2002
  • In an effort to make a right choice among ratio estimation strategies and PPS sampling strategies, we conduct an empirical investigation of the relative performances of three ratio estimation strategies and four PPS estimation strategies using a set of 12 natural populations. The quality of a strategy is measured in the traditional way, namely with the consideration of efficiency, achieved coverage rate of the nominal 99% confidence interval and approach to normality (asymmetry).

이변량 지수모형에서 병렬시스템의 신뢰도 추정 : 이변량 1종 중단 자료이용 (The Reliability Estimation of Parallel System in Bivariate Exponential Model : Using Bivariate Type 1 Censored Data)

  • 조장식;김희재
    • 품질경영학회지
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    • 제25권4호
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    • pp.79-87
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    • 1997
  • In this paper, we obtain maximum likelihood estimator(MLE) of a parallel system reliability for the Marshall and Olkin's bivariate exponential model with birariate type 1 consored data. The asymptotic normal distribution of the estimator is obtained. Also we construct an a, pp.oximate confidence interval for the reliability based on MLE. We present a numerical study for obtaining MLE and a, pp.oximate confidence interval of the reliability.

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Estimating the Difference of Two Normal Means

  • M. Aimahmeed;M. S. Son;H. I. Hamdy
    • Communications for Statistical Applications and Methods
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    • 제7권1호
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    • pp.297-312
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    • 2000
  • A three stage sampling procedure designed to estimate the difference betweentwo normal means is proposed and evaluated within a unified decision-theoretic framework. Both point and fixed-width confidence interval estimation are combined in a single decision rule to make full use of the available data. Adjustments to previous solutions focusing on only one of the latter objectives are indicated. The sensitivity of the confidence interval for detecting shifts in true mean difference is also investigated Numerical and simulation studies are presented to supplement the theoretical results.

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Bootstrap Confidence Interval of Treatment Effect for Censored Data

  • Hyun Jong KIM;Sang Gue PARK
    • Communications for Statistical Applications and Methods
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    • 제4권3호
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    • pp.917-927
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    • 1997
  • Consider the confidence interval estimators of treatment effect when some of data to be analyzed are randomly censored, assuming two-sample location-shift model. Recently proposed PARK and PARK(1995) Estimators is discussed and a bootstrap estimator is proposed. This estimator is compared with other well-known estimators throught the simulation studies and recommendations about the use are made.

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퍼지회귀계수에 관한 퍼지검정 (Fuzzy Test for the Fuzzy Regression Coefficient)

  • 강만기;정지영;최규탁
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 2001년도 춘계학술대회 학술발표 논문집
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    • pp.29-33
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    • 2001
  • We propose fuzzy least-squares regression analysis by few error term data and test the slop by fuzzy hypotheses membership function for fuzzy number data with agreement index. Finding the agreement index by area for fuzzy hypotheses membership function and membership function of confidence interval, we obtain the results to acceptance or reject for the test of fuzzy hypotheses.

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PROC MIXED를 활용한 혼합모형의 신뢰구간추정 (Interval Estimation in Mixed Model by Use of PROC MIXED)

  • 박동준
    • 응용통계연구
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    • 제19권2호
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    • pp.349-360
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    • 2006
  • SAS의 PROC MIXED를 사용하면 일반적인 ANOVA 추정량뿐만 아니라 더 많은 장점을 갖는 제한최대우도추정법 또는 최대우도추정법으로 모수들을 추론할 수 있다. 혼합모형에 속하는 불균형중첩오차구조를 갖는 선형회귀모형에서 랜덤효과와 관련된 그룹간 분산의 신뢰 구간과 고정효과에 해당되는 회귀 계수들에 대 한 신뢰구간을 구하기 위하여 세 가지 크기를 갖는 표본에 대하여 PROC MIXED를 사용하였다. 모의실험을 실행한 결과, 대표본인 경우에는 모수들의 신뢰 구간을 구하기 위하여 PROC MIXED를 활용할 수 있지만, 소표본인 경우에는 PROC MIXED를 사용할 경우, 그룹간 분산의 신뢰 구간과 회귀계수 가운데 절편항의 신뢰구간은 주어진 신뢰계수를 지키지 못하는 것을 보인다.

Bootstrap and Delete-d Jackknife Confidence Intervals for Parameters of an Exponential Distribution

  • Kang, Suk-Bok;Cho, Young-Suk
    • Journal of the Korean Data and Information Science Society
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    • 제8권1호
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    • pp.59-70
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    • 1997
  • We introduce several estimators of the location and the scale parameters of the two-parameter exponential distribution, and then compare these estimators by the mean square error (MSE). Using the parametric bootstrap estimators and the delete-d jackknife, we obtain the bootstrap and the delete-d jackknife confidence intervals for the location and the scale parameters and compare the bootstrap confidence intervals with the delete-d jackknife confidence intervals by length and coverage probability through Monte Carlo method.

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Bootstrap Confidence Intervals for Reliability in 1-way ANOVA Random Model

  • Dal Ho Kim;Jang Sik Cho
    • Communications for Statistical Applications and Methods
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    • 제3권1호
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    • pp.87-99
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    • 1996
  • We construct bootstrap confidence intervals for reliability, R= P{X>Y}, where X and Y are independent normal random variables. One way ANOVA random effect models are assumed for the populations of X and Y, where standard deviations $\sigma_{x}$ and $\sigma_{y}$ are unequal. We investigate the accuracy of the proposed bootstrap confidence intervals and classical confidence intervals work better than classical confidence interval for small sample and/or large value of R.

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