• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.409-418
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• 2006

We propose a nonparametric test procedure for the K-sample problem with grouped data. We construct the test statistics using the scores derived for the linear model based on likelihood ratio principle and obtain asymptotic distribution. Also we illustrate our procedure with an example. Finally we discuss some concluding remarks.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.545-557
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• 2018

The inclusion of covariates in the model often affects not only the estimates of meaningful variables of interest but also its statistical significance. Such gap between statistical and subject-matter significance is a critical issue in huge sample studies. A popular huge sample study, the sample cohort data from Korean National Health Insurance Service, showed such gap of significance in the inference for the effect of obesity on cause of mortality, requiring careful consideration. In this regard, this paper proposes a sample size calibration method based on a Monte Carlo t (or z)-test approach without Monte Carlo simulation, and also proposes a test procedure for subject-matter significance using this calibration method in order to complement the deflated p-value in the huge sample size. Our calibration method shows no subject-matter significance of the obesity paradox regardless of race, sex, and age groups, unlike traditional statistical suggestions based on p-values.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.3
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• pp.193-195
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• 2009

Purpose: The work flow of international hospital's laboratory consists of rapid test and result report at the present day. However, the frequent inquiry of sample, which cause affairs to delay and efficiency to Lower, affects medical examination. In order to promote work's efficiency, we should improve the problem and make work smooth between a laboratory, outs and ward. Materials and Methods: This study runs as follows. First, Investigating test result, test schedule, test receipt, quick result, etc through the activity required from September to November 2007 about the inquiry of sample. After analysis of the problem in December, remaking the test schedule for improvement solution and reporting it to outs and ward. When the result is retest and dilution, we directly fill in a result space with the result situation of the patient to let them know beforehand. We also, prevent the omission of the result through checking the sample list and discriminate in vivo from in vitro by changing the laboratory's telephone number. We have improved the problem about the inquiry of sample through valuation and analysis since the improvement activity from January to March 2008. Result: The case about the frequent inquiry of sample has reduced by 57.8%. this improvement activity indicated that p-value<0.05 was statistically significant through paired t-test. This activity make study smooth and we rapidly report the result. Conclusion: By reducing the case regarding the inquiry of sample, work discontinuation, and concentration reduction, the work efficiency was increased.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.2
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• pp.78-88
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• 2009

In many face recognition problems, the number of available images is limited compared to the dimension of the input space which is usually equal to the number of pixels. This problem is called as the 'small sample size' problem and regularization methods are typically used to solve this problem in feature extraction methods such as LDA. By using regularization methods, the modified within class matrix becomes nonsingu1ar and LDA can be performed in its original form. However, in the process of adding a scaled version of the identity matrix to the original within scatter matrix, the scale factor should be set heuristically and the performance of the recognition system depends on highly the value of the scalar factor. By using the proposed resampling method, we can generate a set of images similar to but slightly different from the original image. With the increased number of images, the small sample size problem is alleviated and the classification performance increases. Unlike regularization method, the resampling method does not suffer from the heuristic setting of the parameter producing better performance.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.237-250
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• 2009

We consider a nonparametric test procedure for the multi-sample problem with grouped data. We construct the test statistics based on the scores obtained from the likelihood ratio principle and derive the limiting distribution under the null hypothesis. Also we illustrate our procedure with an example and obtain the asymptotic properties under the Pitman translation alternatives. Also we discuss some concluding remarks. Finally we derive the covariance between components in the Appendix.

• Communications for Statistical Applications and Methods
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• v.3 no.1
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• pp.207-217
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• 1996

The problem of detecting change with independent data is considered. The asymptotic distribution of the sample change process with the sample Fourier coefficients is shown as a Brownian Bridge process. We suggest to use dynamic statistics such as a sample Brownian Bridge and graphs as statistical animation. Graphs including change PP plots are given by way of illustration with the simulated data.

• Journal of the Korean Statistical Society
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• v.28 no.1
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• pp.107-124
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• 1999

An approximate Bayes criterion for multivariate Behrens-Fisher problem is proposed and examined. Development of the criterion involves derivation of approximate Bayes factor using the imaginary training sample approach introduced by Speigelhalter and Smith (1982). The criterion is designed to develop a Bayesian test, so that it provides an alternative test to other tests based upon asymptotic sampling theory (such as the tests suggested by Bennett(1951), James(1954) and Yao(1965). For the derived criterion, numerical studies demonstrate routine application and give comparisons with the classical tests.

• Journal of Korean Society for Quality Management
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• v.33 no.3
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• pp.149-155
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• 2005

Cusum control chart is used for the purpose of controling the process mean. We consider the problem related to cusum chart for controling process variance. Previous researches have considered the same problem. The main difficulty shown in the related researches was to derive the ARL function which characterizes the properties of the chart. Sample variance, differently with sample mean, follows chi-squared type distribution, even when the quality characteristics are assumed to be normally distributed. The ARL function of cusum is described by a type of integral equation. Since the solution of the integral equation for non-normal distribution is not known well, people used simulation method instead of solving the integral equation directly, or approximation method by taking logarithm of the sample variance. Recently a new method to solve the integral equation for Erlang distribution was published. Here we consider the steps to apply the solution to the problem of controling process variance.

• Proceedings of the Korean Society for Quality Management Conference
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• 2006.04a
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• pp.135-141
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• 2006

Cusum control chart is used for the purpose of controling the process mean. We consider the problem related to cusum chart for controling process variance. Previous researches have considered the same problem. The main difficulty shown in the related researches was to derive the ARL function which characterizes the properties of the chart. Sample variance, differently with sample mean, follows chi-squared type distribution, even when the quality characteristics are assumed to be normally distributed. The ARL function of cusum is described by a type of integral equation. Since the solution of the integral equation for non-normal distribution is not known well, people used simulation method instead of solving the integral equation directly, or approximation method by taking logarithm of the sample variance. Recently a new method to solve the integral equation for Erlang distribution was published. Here we consider the steps to apply the solution to the problem of controling process variance.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1545-1546
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• 2008

In this study, an image processing method with FRANSAC(Fuzzy RANSAC) is presented and discussed for the development of a microscopic gap measuring algorithm. Many problems in edge detection processing are mainly occurred by the illumination system. A serious problem is that the edge set of gap could include the error elements that have relatively larger error than normal. This problem leads to a incorrect measurement of gap. We present a gap measuring algorithm using FRANSAC[1] that is a representative robust estimation algorithm. FRANSAC is peformed by first categorizing all data into good sample set, bad sample set and vague sample set using a fuzzy classification and then sampling in only good sample set. Experimental results show that the presented gap measuring algorithm gives a higher accurate value of gap especially for the more noisy image data.