• Journal of the Korean Statistical Society
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• 제30권4호
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• pp.679-691
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• 2001

Two truncated sequential tests are developed for the two-sample scale problem based on the usual Wilcoxon rank-sum statistic for two different dispersion indices - absolute median deviations, when the medians of the two populations X and Y are equal or known and sums of squared mean deviations, when the medians are either unknown or unequal. The first test is briefly called SWAMD test and the second SWSMD test. For the SWAMD test, the percentile points for both the one-sided and two-sided alternatives, (equation omitted) have been found by Wiener approximation and their values computed for a range of values of a and N; analytical expression for the power function has been derived through Wiener process and its performance studied for various sequential designs for exponential distribution. This test has been illustrated by a numerical example. All the results of the SWAMD test, being directly applicable to the SWSMD test, are not dealt with separately Both the tests are compared and their suitable applications indicated.

• Journal of the Korean Statistical Society
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• 제13권1호
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• pp.32-41
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• 1984

An adaptive distribution-free test is proposed for testing the equality of k independent distributions against unrestricted alternatives. In this paper, several rank-sum test statistics are considered as teh components of the adaptive one. The emprical powers of the adaptive testing procedure are compared to those of the classical F test and the component tests through a Monte Carlo study. The results show that the adaptive test has good power properties over a wide class of underlying distributions.

• Communications for Statistical Applications and Methods
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• 제7권1호
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• pp.107-118
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• 2000

Due to the close relation between cointegration test and multiple unit roots test multiple unit roots test are greatly studied by many researchers,. In this paper we suggest the symmetric type unit roots test which is an adjusted method of Shin (1999) Also we have a small Monte-Carlo simulation study to compare the power of the statistic developed in this paper with those of Shin (1999) and adjusted Fuller statistic(1996)

• Communications for Statistical Applications and Methods
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• 제11권3호
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• pp.643-655
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• 2004

L$_1$-estimator in the linear regression model is widely recognized to have superior robustness in the presence of vertical outliers. While the L$_1$-estimation procedures and algorithms have been developed quite well, less progress has been made with the hypothesis test in the multiple L$_1$-regression. This article suggests computer-intensive resampling approaches, jackknife and bootstrap methods, to estimating the variance of L$_1$-estimator and the scale parameter that are required to compute the test statistics. Monte Carlo simulation studies are performed to measure the power of tests in small samples. The simulation results indicate that bootstrap estimation method is the most powerful one when it is employed to the likelihood ratio test.

• 응용통계연구
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• 제23권5호
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• pp.883-890
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• 2010

Since Fisher's exact test is conducted conditional on the observed value of the margin, there are two kinds of the exact power, the conditional and the unconditional exact power. The conditional exact power is computed at a given value of the margin whereas the unconditional exact power is calculated by incorporating the uncertainty of the margin. Although the sample size is determined based on the unconditional exact power, the actual power which Fisher's exact test has is the conditional power after the experiment is finished. This paper investigates differences between the conditional and unconditional exact power Fisher's exact test. We conclude that such discrepancy is a disadvantage of Fisher's exact test.

• Communications for Statistical Applications and Methods
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• 제24권3호
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• pp.193-209
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• 2017

The power Lindley distribution with some of its properties is considered in this article. Maximum likelihood, least squares, maximum product spacings, and Bayes estimators are proposed to estimate all the unknown parameters of the power Lindley distribution. Lindley's approximation and Markov chain Monte Carlo techniques are utilized for Bayesian calculations since posterior distribution cannot be reduced to standard distribution. The performances of the proposed estimators are compared based on simulated samples. The waiting times of research articles to be accepted in statistical journals are fitted to the power Lindley distribution with other competing distributions. Chi-square statistic, Kolmogorov-Smirnov statistic, Akaike information criterion and Bayesian information criterion are used to access goodness-of-fit. It was found that the power Lindley distribution gives a better fit for the data than other distributions.

• Journal of the Korean Statistical Society
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• 제23권1호
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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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• 제27권4호
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• pp.495-506
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• 1998

The proposed class of generalized logistic models, indexed by an extra parameter, can be used to model or to examine symmetric or asymmetric discrepancies from the logistic model. When there are a finite number of different design points, we are mainly concerned with maximum likelihood estimation of parameters and in deriving their large sample behavior A score test and a bootstrap hypothesis test are also considered to check if the standard logistic model is appropriate to fit the data or if a generalization is needed .

• Journal of the Korean Statistical Society
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• 제30권4호
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• pp.645-660
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• 2001

In this paper we propose a goodness-of-fit test statistic for testing the (null) parametric model versus the (alternative) nonparametric model. Most of existing nonparametric test statistics are based on the residuals which are obtained by regressing the data to a parametric model. Our test is based on the bootstrap estimator of the probability that the smoothing parameter estimator is infinite when fitting residuals to cubic smoothing spline. Power performance of this test is investigated and is compared with many other tests. Illustrative examples based on real data sets are given.

• Communications for Statistical Applications and Methods
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• 제8권2호
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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.