• Journal of the Korean Statistical Society
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• 제31권1호
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• pp.33-43
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• 2002

The empirical Bayes linear loss two-action problem is studied. An empirical Bayes test $\delta$$_{n}$ $^{*}$ is proposed. It is shown that $\delta$$_{n}$ $^{*}$ is asymptotically optimal in the sense that its regret converges to zero at a rate $n^{-1}$ over a class of priors and the rate $n^{-1}$ is the optimal rate of convergence of empirical Bayes tests.sts.

• Journal of the Korean Data and Information Science Society
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• 제17권1호
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• pp.31-39
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• 2006

In many cases, we frequently get a desired information based on the appropriate statistical analysis of collected data sets. Lots of statistical theory rely on the assumption of the normality of the data. In this paper, we compare the empirical power of some normality tests including sample entropy quantity. Monte carlo simulation is conducted for the calculation of empirical power of considered normality tests by varying sample sizes for various distributions.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2001년도 봄 학술발표회 논문집
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• pp.215-222
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• 2001

Uncertainty study of shear strength characteristics of the marine clays was carried out based ell In-situ tests and laboratory tests on tile south-east coastal region of the Korean peninsula. Theoretical analyses were studied using both tile spherical cavity expansion theory in finite soil mass and the strain path method to determine tile cone factor using the undrained shear strengths obtained by in-situ tests, and the empirical methods in accordance with the ultimate resistance theory were also discussed. Analysis show that the empirical methods suggest more reasonable value than that of theoretical methods in terms of comparing the cone factor estimated using linear regression and frequency distribution analyses. The cone factors obtained by the empirical methods are 18, 15, and 6 respectively, from the results of total cone resistance, effective cone resistance, and excess porewater cone resistance method, and the estimated were similar to those of previous researcher's.

• 응용통계연구
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• 제24권6호
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• pp.1197-1211
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• 2011

The actual power performance of historical structural change tests are compared under various alternatives. The tests of interest are F, CUSUM, MOSUM, Moving Estimates and empirical distribution function tests with both recursive and ordinary least-squares residuals. Our comparison of the structural tests involves limiting distributions under the hypothesis, the ability to detect the alternative hypotheses under one or double structural change, and smooth change in parameters. Even though no version is uniformly superior to the other, the knowledge about the properties of those tests and connections between these tests can be used in practical structural change tests and in further research on other change tests.

• Communications for Statistical Applications and Methods
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• 제23권3호
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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.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2005년도 총회 및 춘계학술발표회
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• pp.446-450
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• 2005

Hydraulic conductivity of unconsolidated media can be determined by aquifer tests, laboratory tests and empirical equations based on grain size analysis. Commonly, the different methods give different hydraulic conductivities. Grain size measurements were done to determine hydraulic conductivity, using 184 soil samples collected from eight boreholes in a riverbank filtration area, Daesan-Myeon, Changwon City, Korea, Pumping tests were conducted at the riverbank filtration area. The average hydraulic conductivity by the empirical relations from grain size measurements comes out around $10^{-2}m/s$, 22 to 55 times higher than by the pumping test analyses. The hydraulic conductivity obtained from the empirical equations is interpreted to have a relationship with steady-state condition while that obtained from the pumping tests is interpreted to have a relationship with unsteady-state condition. Thus, hydraulic conductivity obtained from various methods should be critically analyzed for reasonable management of groundwater development.

• Journal of the Korean Data and Information Science Society
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• 제27권5호
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• pp.1399-1412
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• 2016

We compare many normality tests consisting of different sources of information extracted from the given data: Anderson-Darling test, Kolmogorov-Smirnov test, Cramervon Mises test, Shapiro-Wilk test, Shaprio-Francia test, Lilliefors, Jarque-Bera test, D'Agostino' D, Doornik-Hansen test, Energy test and Martinzez-Iglewicz test. For the purpose of comparison, those tests are applied to the various types of data generated from skewed distribution, unsymmetric distribution, and distribution with different length of support. We then summarize comparison results in terms of two things: type I error control and power. The selection of the best test depends on the shape of the distribution of the data, implying that there is no test which is the most powerful for all distributions.

• Communications for Statistical Applications and Methods
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• 제20권3호
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• pp.199-205
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• 2013

We employ sieve bootstraps for empirical likelihood tests in time series models because their null distributions are often vulnerable to the presence of serial dependence. We found a significant size refinement of the bootstrapped versions of a Lagrangian Multiplier type test statistic regardless of the bandwidth choice required by long-run variance estimations.

• Journal of the Korean Statistical Society
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• 제25권3호
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• pp.337-346
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• 1996

The chi-squared type statistic generated from the empirical process can be used for testing the goodness of fit hypothesis on iid random sample. Lee (1995) showed that under some conditions, the chi-squared type statistic is asymptotically maximin in the sense of Strasser (1985). Since the chi-squared type statistic depends on the choice of *points in the unit interval, it is worth investigating the points yielding more efficient tests. Motivated by this viewpoint, we are led to study the asymptotic relative efficiency of chi-squared type tests in the same setting of Lee (1995). Some examples are given for illustration.

• Communications for Statistical Applications and Methods
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• 제31권1호
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• pp.1-35
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• 2024

The assumption of homoscedasticity is one of the most crucial assumptions for many parametric tests used in the biological sciences. The aim of this paper is to compare the empirical probability of type I error and the power of ten parametric and two non-parametric tests for homoscedasticity with simulations under different types of distributions, number of groups, number of samples per group, variance ratio and significance levels, as well as through empirical data from an agricultural experiment. According to the findings of the simulation study, when there is no violation of the assumption of normality and the groups have equal variances and equal number of samples, the Bhandary-Dai, Cochran's C, Hartley's Fmax, Levene (trimmed mean) and Bartlett tests are considered robust. The Levene (absolute and square deviations) tests show a high probability of type I error in a small number of samples, which increases as the number of groups rises. When data groups display a nonnormal distribution, researchers should utilize the Levene (trimmed mean), O'Brien and Brown-Forsythe tests. On the other hand, if the assumption of normality is not violated but diagnostic plots indicate unequal variances between groups, researchers are advised to use the Bartlett, Z-variance, Bhandary-Dai and Levene (trimmed mean) tests. Assessing the tests being considered, the test that stands out as the most well-rounded choice is the Levene's test (trimmed mean), which provides satisfactory type I error control and relatively high power. According to the findings of the study and for the scenarios considered, the two non-parametric tests are not recommended. In conclusion, it is suggested to initially check for normality and consider the number of samples per group before choosing the most appropriate test for homoscedasticity.