• Title/Summary/Keyword: Intraclass Correlation Coefficient

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Bayesian Hypothesis Testing for Intraclass Correlation Coefficient

  • Lee, Seung-A;Kim, Dal-Ho
    • Communications for Statistical Applications and Methods
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    • v.13 no.3
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    • pp.551-566
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    • 2006
  • In this paper, we consider a Bayesian model selection for the intraclass correlation coefficient in familiar data. In particular, we compare two nested models such as the independence and intraclass models using the reference prior. A criterion for testing is the Bayesian Reference Criterion by Bernardo (1999) and the Intrinsic Bayes Factor by Berger and Pericchi (1996). We provide numerical examples using simulation data sets for illustration.

Bayesian Test for the Intraclass Correlation Coefficient in the One-Way Random Effect Model

  • Kang, Sang-Gil;Lee, Hee-Choon
    • Journal of the Korean Data and Information Science Society
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    • v.15 no.3
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    • pp.645-654
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    • 2004
  • In this paper, we develop the Bayesian test procedure for the intraclass correlation coefficient in the unbalanced one-way random effect model based on the reference priors. That is, the objective is to compare two nested model such as the independent and intraclass models using the factional Bayes factor. Thus the model comparison problem in this case amounts to testing the hypotheses $H_1:\rho=0$ versus $H_2:{\rho}{\neq}0$. Some real data examples are provided.

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Saddlepoint Approximation to Quadratic Form and Application to Intraclass Correlation Coefficient

  • Na, Jong-Hwa
    • Journal of the Korean Data and Information Science Society
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    • v.19 no.2
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    • pp.497-504
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    • 2008
  • In this paper we studied the saddlepoint approximations to the distribution of quadratic forms in normal variables. We derived the approximations as a special case of Na & Kim (2005). Also applications to a statistic which concerns intraclass correlation coefficient are presented. Simulations show the accuracy and availability of the suggested approximations.

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Noninformative Priors for the Common Intraclass Correlation Coefficient

  • Kim, Dal-Ho
    • Communications for Statistical Applications and Methods
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    • v.18 no.2
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    • pp.189-199
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    • 2011
  • In this paper, we develop the noninformative priors for the common intraclass correlation coefficient when independent samples drawn from multivariate normal populations. We derive the first and second order matching priors. We reveal that the second order matching prior dose not match alternative coverage probabilities up to the second order and is not a HPD matching prior. It turns out that among all of the reference priors, one-at-a-time reference prior satisfies a second order matching criterion. Our simulation study indicates that one-at-a-time reference prior performs better than the other reference priors in terms of matching the target coverage probabilities in a frequentist sense.

Noninformative Priors for the Intraclass Coefficient of a Symmetric Normal Distribution

  • Chang, In-Hong;Kim, Byung-Hwee
    • Proceedings of the Korean Statistical Society Conference
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    • 2003.10a
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    • pp.15-19
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    • 2003
  • In this paper, we develop the Jeffreys' prior, reference priors and the probability matching priors for the intraclass correlation coefficient of a symmetric normal distribution. We next verify propriety of posterior distributions under those noninformative priors. We examine whether reference priors satisfy the probability matching criterion.

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Reliability and Validity of the Korean Western Ontario and McMaster Universities(WOMAC) Osteoarthritis Index in Patients with Osteoarthritis of the Knee (퇴행성 슬관절염 환자에 대한 한글판 WOMAC Index의 신뢰도와 타당성에 관한 연구)

  • Ko, Tae-Sung;Kim, Seong-Yeol;Lee, Jong-Soo
    • Journal of Korean Medicine Rehabilitation
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    • v.19 no.2
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    • pp.251-260
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    • 2009
  • Objectives : To investigate reliability and validity of Korean translation of Western Ontario and McMaster Universities(WOMAC) osteoarthritis index. Methods : The reliablity, construct validity of the Korean WOMAC Index in the patient of knee osteoarthritis was investigated. Test-retest reliability was quantified with pearson's correlation coefficient and intraclass correlation coefficient. Internal consistency was quantified with Cronbach's ${\alpha}$. and construct validity with pearson's correlation coefficient by correlating of the Visual Analog Scale(VAS). Results : Test-retest reliability of Korean WOMAC Index for pain was 0.76 to 0.95, stiffness was 0.89 to 0.94, and physical function was 0.71 to 0.95. Intraclass correlation coefficient for pain was 0.76 to 0.94, stiffness was 0.54 to 0.89, and physical function was 0.70 to 0.95. Internal consistency were 0.94 and 0.94 for the first and second time, respectively. Construct validity for pain was 0.79, for stiffness was 0.66, and physical function was 0.67. Conclusions : The Korean translation of Western Ontario and McMaster Universities(WOMAC) osteoarthritis index is reliable, valid assessment tool in knee osteoarthritis.

DEVELOPING NONINFORMATIVE PRIORS FOR THE FAMILIAL DATA

  • Heo, Jung-Eun;Kim, Yeong-Hwa
    • Journal of the Korean Statistical Society
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    • v.36 no.1
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    • pp.77-91
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    • 2007
  • This paper considers development of noninformative priors for the familial data when the families have equal number of offspring. Several noninformative priors including the widely used Jeffreys' prior as well as the different reference priors are derived. Also, a simultaneously-marginally-probability-matching prior is considered and probability matching priors are derived when the parameter of interest is inter- or intra-class correlation coefficient. The simulation study implemented by Gibbs sampler shows that two-group reference prior is slightly edge over the others in terms of coverage probability.

Comparison of Correlation Coefficients and Intraclass Correlation Coefficients Between Two-way FSI Flow Velocity of Simulated Abdominal Aorta and Human 4D Flow MRI Flow Velocity (시뮬레이션 복부 대동맥의 양방향 FSI 유속과 인체 4D flow MRI 유속의 상관계수, 급내상관계수 비교)

  • Ahn, Hae Nam;Kim, Jung Hun;Park, Ji eun;Choi, Hyeun Woo;Lee, Jong Min
    • Journal of Biomedical Engineering Research
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    • v.42 no.4
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    • pp.143-149
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    • 2021
  • In order to predict and prevent the disease of the abdominal aorta, which is the largest artery in the human body and the most common aneurysm, the normal arterial blood flow operation should be considered. To this end, we are trying to solve problems that may arise in the future by executing FSI based on the data obtained from 4D flow MRI. However, to match the similarity between the 4D flow MRI flow and the FSI flow, correlation was used in previous papers, but the correlation did not show the degree of agreement. Therefore, in this paper, we analyzed the correlation between the 4D flow MRI flow velocity of the human abdominal aorta and the two-way FSI flow velocity in which the three physical properties used for the aortic FSI were added to the CT abdominal aorta 3D model and the interclass correlation coefficient. As a result, the physical property M2 showed the highest similarity in correlation and intraclass correlation coefficient, and this property is intended to be helpful in the future study of the abdominal aortic two-way FSI flow rate.

Detecting the Influential Observation Using Intrinsic Bayes Factors

  • Chung, Younshik
    • Journal of the Korean Statistical Society
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    • v.29 no.1
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    • pp.81-94
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    • 2000
  • For the balanced variance component model, sometimes intraclass correlation coefficient is of interest. If there is little information about the parameter, then the reference prior(Berger and Bernardo, 1992) is widely used. Pettit nd Young(1990) considered a measrue of the effect of a single observation on a logarithmic Bayes factor. However, under such a reference prior, the Bayes factor depends on the ratio of unspecified constants. In order to discard this problem, influence diagnostic measures using the intrinsic Bayes factor(Berger and Pericchi, 1996) is presented. Finally, one simulated dataset is provided which illustrates the methodology with appropriate simulation based computational formulas. In order to overcome the difficult Bayesian computation, MCMC methods, such as Gibbs sampler(Gelfand and Smith, 1990) and Metropolis algorithm, are empolyed.

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