• Journal of Oriental Neuropsychiatry
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• v.18 no.3
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• pp.15-21
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• 2007

This study was done to review problems in terms of statistical description and statistical application and analysis. Methods; The authors reviewed 42 statistical clinical study papers excluding 12 Overview papers, 75 Descriptive papers, 48 Animal studies out of 177 papers in the Journal of Oriental Neuropsychiatry in the 5 years from 2002 to 2006. Results : 1) 3 papers(7.1%) had no description of statistical method, only P-values, 25 papaers(59.5%) had tables without description of statistical method, 1 paper (2.3%) had no description of statistical method in study method. 2) 10 papers(23.8%) contained problems in terms of statistical application and analysis. 6papers (6/23, 26.0%) for Student t-test, 2 papers(2/7 28.6%)for $X^2$- test, 1 paper(1/15 6.7%) for the analysis of variance, 1 paper(1/6 16.7%) for Pearson correlation contained statistical problems. Conclusion : It was suggested that consultation of investigators with statisticians and more extensive statistical refereeing, the form of the guidelines for description and application of statistics are needed.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.47-55
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• 2012

The newly revised bioequivalence guideline of Korea allows the add-on test since July 1, 2008 when the initial bioequivalence trial fails to show the equivalence of two drugs. The statistical model of the add-on test and its two stage testing procedures are discussed. Some statistical points of consistency test in the add-on test are considered and the issue on the sample size of add-on test is discussed. Some reasonable alternative like Japan's guideline for bioequivalence studies is recommended to secure the proper use of an add-on study through some simulation studies.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.639-647
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• 2002

Efforts to obtain more power for unit root tests have continued. Pantula at el.(1994) compared empirical powers of several unit root test statistics and addressed that the weighted symmetric estimator(WSE) and the unconditional maximum likelihood estimator(UMLE) are the best among them. One can easily see that the powers of these two statistics are almost the same. In this paper we explain a connection between WSE and UMLE and suggest a unit root test statistic which may explain the connection between them.

• Journal of the Korean Statistical Society
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• v.32 no.2
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• pp.93-101
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• 2003

This paper deals with testing the equality of the coefficients of variation in two inverse Gaussian populations. The likelihood ratio, Lagrange-multiplier and Wald tests are presented. Monte-Carlo simulations are performed to compare the powers of these tests. In a simulation study, the likelihood ratio test appears to be consistently more powerful than the Lagrange-multiplier and Wald tests when sample size is small. The powers of all the tests tend to be similar when sample size increases.

• Journal of the Korean Statistical Society
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• v.30 no.1
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• pp.63-76
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• 2001

An adaptive estimation and testing method is proposed for comparing dispersions among several ordered groups. Based upon the large sampling theory for nonparametric quartile estimators, we derive the order restricted estimators and construct a simple test statistic. This test statistic has a mixture of several chi-square distributions as its asymptotic null distribution. The proposed test is illustratively applied to survival time data for the patients with carcinoma of the oropharynx.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.311-317
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• 2001

It is necessary to check the linearity of selected covariates in regression diagnostics. There are various graphical methods using residual plots such as partial residual plots, augmented partial residual plots and combining conditional expectation and residual plots. In this paper, we propose the modified pseudolikelihood ratio test statistics based on these residual plots to test linearity of selected covariate. These test statistics which measure the distance between the nonparametric and parametric models are derived as a ratio of quadratic forms. The approximate distribution of these statistics is calculated numerically by using three moments. The power comparison of these statistics is given.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.483-497
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• 2001

When one fits a parametric density function to a data set, it is usually advisable to test the goodness of the postulated model. In this paper we study the nonparametric tests for testing the null hypothesis against general alternatives, when the null hypothesis specifies the density function up to unknown parameters. We modify the test statistic which was proposed by the first author and his colleagues. Asymptotic distribution of the modified statistic is derived and its performance is compared with some other tests through simulation.

• Journal of the Korean Statistical Society
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• v.23 no.1
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• pp.179-185
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• 1994

To test the equality of survival distributions in the presence of arbitrary right censorship, the choice of weights which are functions of the number of individuals at risk at the time of each death is very important in increasing the power of the test. In this paper a weight by probit scale is derived and the efficiencies relative to the other weight's are also investigated.

• Journal of the Korean Statistical Society
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• v.23 no.1
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• pp.207-216
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• 1994

Count data are often overdispersed, and an appropriate test for the existence of the overdispersion is necessary. In this paper we derive a score test based on the extended quasi-likelihood and the pseudolikelihood after adjusting to the Bartlett factor. Also, we compare it with Levene (1960)'s F-type test suggested by Ganio and Schafer (1992).

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.467-477
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• 2006

A multidimensionality detecting procedure DETECT, based on conditional covariances between items, is extended and refined to deal with polytomous item data as well as binary one. A large body of simulation study shows extraordinary performance of DETECT in both enumerating degrees of multidimensionality in a test and discovering dimensionally distinctive item clusters. Real data study also provides very meaningful results, making DETECT a strong dimensionality assessment tool for the test data analysis.