• Structural Engineering and Mechanics
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• 제81권1호
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• pp.103-115
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• 2022

The prediction error variances for frequencies are usually considered as unknown in the Bayesian system identification process. However, the error variances for mode shapes are taken as known to reduce the dimension of an identification problem. The present study attempts to explore the effectiveness of Bayesian approach of model parameters updating using Markov Chain Monte Carlo (MCMC) technique considering the prediction error variances for both the frequencies and mode shapes. To remove the ergodicity of Markov Chain, the posterior distribution is obtained by Gaussian Random walk over the proposal distribution. The prior distributions of prediction error variances of modal evidences are implemented through inverse gamma distribution to assess the effectiveness of estimation of posterior values of model parameters. The issue of incomplete data that makes the problem ill-conditioned and the associated singularity problem is prudently dealt in by adopting a regularization technique. The proposed approach is demonstrated numerically by considering an eight-storey frame model with both complete and incomplete modal data sets. Further, to study the effectiveness of the proposed approach, a comparative study with regard to accuracy and computational efficacy of the proposed approach is made with the Sequential Monte Carlo approach of model parameter updating.

• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.553-566
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• 2003

In many cases, the measurement error variances may be functions of the unknown true values or related covariates. This paper considers design-based estimators of the parameters of these variance functions based on the within-unit sample variances. This paper devotes to: (1) define an error scale factor $\delta$; (2) develop estimators of the parameters of the linear measurement error variance function of the true values under large-sample and small-error conditions; (3) use propensity methods to adjust survey weights to account for possible selection effects at the replicate level. The proposed methods are applied to medical examination data from the U.S. Third National Health and Nutrition Examination Survey (NHANES III).

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2003년도 춘계학술대회
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• pp.1-14
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• 2003

In man cases, the measurement error variances may be functions of the unknown true values or related covariate. In some cases, the measurement error variances increase in proportion to the value of predictor. This paper develops estimators of the parameters of a linear measurement error variance function under stratified multistage random sampling design and additional conditions. Also, this paper evaluates and compares the power of an asymptotically unbiased test with that of an asymptotically biased test. The proposed method are applied to blood sample measurements from the U.S. Third National Health and Nutrition Examination Survey(NHANES III)

• Journal of the Korean Statistical Society
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• 제23권1호
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• pp.115-134
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• 1994

Polynomial errors-in-variables model with one predictor variable and one response variable is defined and an estimator of model is derived following the Booth's linear model estimation procedure. Since polynomial model is nonlinear function of the unknown regression coefficients and error-free predictors, it is nonlinear model in errors-in-variables model. As a result of applying linear model estimation method to nonlinear model, some additional assumptions are necessary. Hence, an estimator is derived under the assumption that the error variances are decrasing as sample size increases. Asymptotic propoerties of the derived estimator are provided. A simulation study is presented to compare the small sample properties of the derived estimator with those of OLS estimator.

• 통합자연과학논문집
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• 제12권3호
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• pp.91-99
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• 2019

Many researchers in various study fields use the two sample t-test to confirm their treatment effects. The two sample t-test is generally used for small samples, and assumes that two independent random samples are selected from normal populations, and the population variances are unknown. Researchers often conduct F-test, the test of equality of variances, before testing the treatment effects, and the test statistic or confidence interval for the two sample t-test has two formats according to whether the variances are equal or not. Researchers using the two sample t-test often want to know how large sample sizes they need to get reliable test results. This research gives some guidelines for sample sizes to them through simulation works. The simulation had run for normal populations with the different ratios of two variances for different sample sizes (${\leq}30$). The simulation results are as follows. First, if one has no idea equality of variances but he/she can assume the difference is moderate, it is safe to use sample size at least 20 in terms of the nominal level of significance. Second, the power of F-test for the equality of variances is very low when the sample sizes are small (<30) even though the ratio of two variances is equal to 2. Third, the sample sizes at least 10 for the two sample t-test are recommendable in terms of the nominal level of significance and the error limit.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1519-1522
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• 2002

In this paper, we propose a new adaptive dissolve detection method based on the analysis of a dissolve modeling error that is the difference between an ideally modeled dissolve curve without any correlation and an actual variance curve with a correlation. The dissolve modeling error is determined based on a correlation between two scenes and variances for each scene. First, Candidate regions are extracted by using the characteristics of a parabola that is downward convex, then the candidate region will be verified based on a dissolve modeling error. If a dissolve modeling error on a candidate region is less than a threshold that is defined by a dissolve modeling error with a target correlation, the candidate region should be a dissolve region with a correlation less than the target correlation. The threshold is adaptively determined based on the variances between the candidate regions and the target correlation. By considering the correlation between neighbor scenes, the proposed method is able to be a semantic scene-change detector. The proposed algorithm was tested on various types of data and its performance proved to be more accurate and reliable when compared with other commonly used methods

• Journal of the Korean Statistical Society
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• 제24권2호
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• pp.551-562
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• 1995

We consider the iterated bootstrap method in regression model with heterogeneous error variances. The iterated wild bootstrap confidence intervla of the mean response is considered. It is shown that the iterated wild bootstrap confidence interval has coverage error of order $n^{-1}$ wheresa percentile method interval has an error of order $n^{-1/2}$. The simulation results reveal that the iterated bootstrap method calibrates the coverage error of percentile method interval successfully even for the small sample size.

• International Journal of Aeronautical and Space Sciences
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• 제12권1호
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• pp.37-42
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• 2011

An extended scalar adaptive filter for guided missiles using a global positioning system receiver is presented. A conventional scalar adaptive filter is adequate filter for eliminating sudden abnormal jumping measurements. However, if missile or vehicle velocities have variation, the conventional filter cannot eliminate abnormal measurements. The proposed filter utilizes an acceleration term, which is an improvement not used in previous conventional scalar adaptive filters. The proposed filter continuously estimates noise measurement variance, velocity error variance and acceleration error variance. For estimating the three variances, an innovation method was used in combination with the least square method for the three variances. Results from the simulations indicated that the proposed filter exhibited better position accuracy than the conventional scalar adaptive filter.

• Communications for Statistical Applications and Methods
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• 제17권1호
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• pp.79-88
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• 2010

기초통계학의 수업에서 두 집단간 평균의 차이를 검정함에 있어 두 집단의 분산의 동질성 여부에 따라 다른 통계 절차를 사용할 것을 제안하고 있다. 이러한 이유로 통계 분석에 사용되는 SAS나 SPSS 등의 패키지에서는 두 집단의 평균 차이의 검정에 앞서 분산의 동질성 검정을 선행할 것을 제안한다. 하지만, 이전의 몇몇 연구에서 알려진 바와 같이 이러한 이 단계 검정 절차는 검정의 유의수준(제 1종의 오류)을 제어하기가 어렵다. 본 글에서는 이 단계 검정을 행함에 있어 1 단계와 2 단계의 유의수준 ${\alpha}_1$과 ${\alpha}_2$를 조절하여 전체 검정의 유의수준을 주어진 ${\alpha}$ 이하로 제어하는 절차를 소개한다.

• Journal of the Korean Data and Information Science Society
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• 제7권1호
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• pp.87-92
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• 1996

A regression model with nested erroe structure is considered. The regression model includes two error terms that are independent and normally distributed with zero means and constant variances. This error structure of the model gives correlated response variables. The distributions of variance components in the regression model with nested error structure are dervied by using theorems for quadratic forms.