• Communications for Statistical Applications and Methods
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• v.4 no.2
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• pp.581-588
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• 1997

Univariate F test is based on the multisample sphericity assumption. Robustness for tests of a main effect of the within-subjects factor was investigated when the assumptions of the onmibus F tests are violated in a split-plot design with one between-subjects factor using untrimmed data and trimmed data. The results indicate that when sample sizes are unbalanced and dispersion matrices are heterogeneous, the CIGA and the $CIGA_T$ tests better control Type I error rates than do the $F_T$test and the$\widetilde{\xi_T}$test.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.281-286
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• 2003

The stationarity is one of the most important properties of a time series. We propose robust sign tests for seasonal autoregressive process to determine whether or not a time series is stationary. The tests have an exact binomial null distribution and are robust to the outliers and the heteroscedastic errors. Monte-Carlo simulation shows that the sign test is locally more powerful than the OLSE-based tests for heavy-tailed and/or heteroscedastic error distributions.

• Communications for Statistical Applications and Methods
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• v.23 no.3
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• pp.215-230
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• 2016

The powers of some tests for independence hypothesis against positive (negative) quadrant dependence in generalized Farlie-Gumbel-Morgenstern distribution are compared graphically by simulation. Some of these tests are usual linear rank tests of independence. Two other possible rank tests of independence are locally most powerful rank test and a powerful nonparametric test based on the $Cram{\acute{e}}r-von$ Mises statistic. We also evaluate the empirical power of the class of distribution-free tests proposed by Kochar and Gupta (1987) based on the asymptotic distribution of a U-statistic and the test statistic proposed by $G{\ddot{u}}ven$ and Kotz (2008) in generalized Farlie-Gumbel-Morgenstern distribution. Tests of independence are also compared for sample sizes n = 20, 30, 50, empirically. Finally, we apply two examples to illustrate the results.

• Advances in concrete construction
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• v.3 no.3
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• pp.161-185
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• 2015

FRP confining is a widely used method for seismic retrofitting of concrete columns. Several studies investigated the stress-strain behavior of FRP confined concrete prisms with square and rectangular sections both experimentally and analytically. In some studies, the monotonic stress-strain behavior of confined concrete was investigated and compressive strength models were developed. To study the reliability of these models, thorough statistical tests are required. This paper aims to investigate the reliability of the presented models using statistical tests including t-test, wilcoxon rank sum test, wilcoxon signed rank test and sign test with a level of significance of 5%. Wilk Shapiro test was also employed to evaluate the normality of the data distribution. The results were compared for different cross section and confinement types. To see the accuracy of the models when there were no significant differences between the results, the coefficient of confidence was used.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.639-646
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• 2015

We propose simultaneous tests for mean and variance under the normality assumption. After formulating the null hypothesis and its alternative, we construct test statistics based on the individual p-values for the partial tests with combining functions and derive the null distributions for the combining functions. We then illustrate our procedure with industrial data and compare the efficiency among the combining functions with individual partial ones by obtaining empirical powers through a simulation study. A discussion then follows on the intersection-union test with a combining function and simultaneous confidence region as a simultaneous inference; in addition, we discuss weighted functions and applications to the statistical quality control. Finally we comment on nonparametric simultaneous tests.

• Journal of the Korean Statistical Society
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• v.26 no.2
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• pp.171-180
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• 1997

Effects of order misspecification on statistical behavior of unit root tests are studied. We derive the limiting distributions of the Dickey-Fuller test statistics whose numerators are of the form c .int. W dW + .kappa. where W is a standard Brownian motion on [0, 1] and c is a real number. The term .kappa. is a major consequence of order misspecification and its explict expression is derived. Based on an analysis of .kappa., effects of order misspecification on unit root tests for AR(2), ARMA(1, 1), and AR(3) models are investigated.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.177-182
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• 2012

A structural break in the level as well as in the innovation variance has often been exhibited in economic time series. In this paper we propose robust unit root tests based on a sign-type test statistic when a time series has a shift in its level and the corresponding volatility. The proposed tests are robust to a wide class of partially stationary processes with heavy-tailed errors, and have an exact binomial null distribution. Our tests are not affected by the size or location of the break. We set the structural break under the null and the alternative hypotheses to relieve a possible vagueness in interpreting test results in empirical work. The null hypothesis implies a unit root process with level shifts and the alternative connotes a stationary process with level shifts. The Monte Carlo simulation shows that our tests have stable size than the OLSE based tests.

• Communications for Statistical Applications and Methods
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• v.6 no.2
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• pp.565-584
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• 1999

the covariance process of two martingales provides a useful tool to capture the dependence structure for paired censored data. in this paper it is applied to modify the variances of weighted logrank tests in order to take account of dependence between paired subjects. In the process of modification a 'variance correction term' is introduced. Some variance estimators based on separate samples are considered together. Performance of the estimators are compared through simulation studies. Several independence tests for bivariate sruvival date are also proposed which are naturally reduced from the weighted logrank tests accomodating dependence structure. Simulation studies are carried out to compare the independence tests. Both the weighted logrank tests and the independence tests are illustrated by an example.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.137-145
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• 2001

There are many situations in which the well-known tests such as log-rank test and Gehan-Wilcoxon test fail to detect the survival differences. Assuming large samples, these tests are developed asymptotically normal properties. Thus, they shall be called asymptotic tests in this paper, Several asymptotic tests sensitive to some specific types of survival differences have been recently proposed. This paper compares by simulations the test levels and the powers of the conventional asymptotic tests and their random permutation versions. Simulation studies show that the random permutation tests possess competitive powers compared to the corresponding asymptotic tests, keeping exact test levels even in the small sample case. It also provides the guidelines for choosing the valid and most powerful test under the given situation.

• Journal of the Korean Statistical Society
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• v.26 no.2
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• pp.211-222
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• 1997

We briefly review three Raleigh type location tests based on direction vectors, which have been shown to be efficient when the distribution is unknown, skewed, or heavy-tailed. Then we calculate their test breakdown points and discuss the robustness of Randles multivariate sign test for one-sample.