• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.1-14
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• 2020

Investigations of biosimilarity between reference drugs and test drugs required statistical tests; in addition, statistical tests to evaluate biosimilarity have been recently proposed. Ordinal outcome data has been observed in research; however, appropriate statistical tests to deal with ordinal endpoints for biosimilar have not yet been proposed. This paper extends existing design for ordinal endpoints. Using measure of nominal-ordinal association and relative distances between drugs are defined so that testing procedures are developed. Through simulation studies, we investigate type I error rate and power to show the performance of our suggested method. Furthermore, a comparison between the statistical tests and other designs is proviede to show significance of ordinal endpoints.

• International Journal of Reliability and Applications
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• v.1 no.2
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• pp.145-154
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• 2000

In this paper, tests of statistical hypotheses for the expectation and variance of Gaussian fuzzy population are discussed.

• Communications for Statistical Applications and Methods
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• v.5 no.2
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• pp.417-429
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• 1998

This paper compares powers of the two nonparametric tests under a variety of population distributions through a simulation study. Both tests require that the two underlying populations have the same variance, but this assumption is relaxed in some of the comparisons.

• Communications for Statistical Applications and Methods
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• v.4 no.2
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• pp.427-434
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• 1997

The purpose of this paper is to discuss distribution-free tests of hypothesis that the random samples are identically distributed against the epidemic alternative. But most tests that have been considered are depended only on specific null distribution. Two nonparametric tests are considered and compared with a likelihood ratio test by the empirical powers.

• Journal of the Korean Statistical Society
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• v.5 no.1
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• pp.3-18
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• 1976

To test simultaneous significance of more than two linear regression coefficients, we can consider multivariate-t tests with critical regions in t-space instead of F-tests where t-values are t-statistics of significance tests of one coefficient. In this paper bivariate-t distributions and bivariate-t tests of two coefficients such as maxmod, minmod, one-tailed maxmod and one-tailed minmod tests are studied. Through the calculation of powers of test, it is learned that in some cases bivariate-t test are more powerful than F-tests.

• Journal of the Korean Statistical Society
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• v.36 no.4
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• pp.447-456
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• 2007

On the basis of marginal likelihood of the residual vector which is free of nuisance mean parameters, we propose new Lagrange Multiplier seasonal unit root tests in seasonal autoregressive process. The limiting null distribution of the tests is the standardized ${\chi}^2-distribution$. A Monte-Carlo simulation shows the new tests are more powerful than the tests based on the ordinary least squares (OLS) estimator, especially for large number of seasons and short time spans.

• Journal of the Korean Statistical Society
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• v.31 no.3
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• pp.301-314
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• 2002

For autoregressive moving average (ARMA) models, robust unit root tests are developed using M-estimators. The tests are parametric in the sense ARMA parameters are estimated jointly with unit roots. A Monte-Carlo experiment reveals superiority of the parametric tests over the semipararmetric tests of Lucas (1995a) in terms of both empirical sizes and powers.

• Journal of the Korean Statistical Society
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• v.30 no.3
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• pp.347-366
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• 2001

Robust analogues of the likelihood ratio test are considered for testing of hypotheses involving multiple discrete distributions. The test statistics are generalizations of the Hellinger deviance test of Simpson(1989) and disparity tests of Lindsay(1994), obtained by looking at a 'penalized' version of the distances; harris and Basu (1994) suggest that the penalty be based on reweighting the empty cells. The results show that often the tests based on the ordinary and penalized distances enjoy better robustness properties than the likelihood ratio test. Also, the tests based on the penalized distances are improvements over those based on the ordinary distances in that they are much closer to the likelihood ratio tests at the null and their convergence to the x$^2$ distribution appears to be dramatically faster; extensive simulation results show that the improvement in performance of the tests due to the penalty is often substantial in small samples.

• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.291-300
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• 2013

In this paper, we propose method-free permutation predictor hypothesis tests in the context of sufficient dimension reduction. Different from an existing method-free bootstrap approach, predictor hypotheses are evaluated based on p-values; therefore, usual statistical practitioners should have a potential preference. Numerical studies validate the developed theories, and real data application is provided.

• Journal of the Korean Statistical Society
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• v.29 no.1
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• pp.63-80
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• 2000

We consider the panel data regression model with nested error componets. In this paper, the several Lagrange Multipler tests for the nested error component model are derived. These tests extend the earlier work of Honda(1985), Moulton and Randolph(1989), Baltagi, et al.(1992) and King and Wu(1997) to the nested error component case. Monte Carlo experiments are conducted to study the performance of these LM tests.