• The Korean Journal of Applied Statistics
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• v.23 no.5
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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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• v.10 no.2
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• pp.277-289
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• 2003

We propose modified exact inferential methods in logistic regression model. Exact conditional distribution in logistic regression model is often highly discrete, and ordinary exact inference in logistic regression is conservative, because of the discreteness of the distribution. For the exact inference in logistic regression model we utilize the modified P-value. The modified P-value can not exceed the ordinary P-value, so the test of size $\alpha$ based on the modified P-value is less conservative. The modified exact confidence interval maintains at least a fixed confidence level but tends to be much narrower. The approach inverts results of a test with a modified P-value utilizing the test statistic and table probabilities in logistic regression model.

• Communications for Statistical Applications and Methods
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• v.21 no.6
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• pp.539-559
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• 2014

We derive the exact distribution of summation for random samples from uniform distribution and then compare the exact distribution with the approximated normal distribution obtained by the central limit theorem. To check the similarity between two distributions, we consider five existing normality tests based on the difference between the target normal distribution and empirical distribution: Anderson-Darling test, Kolmogorov-Smirnov test, Cramer-von Mises test, Shapiro-Wilk test and Shaprio-Francia test. For the purpose of comparison, those normality tests are applied to the simulated data. It can sometimes be difficult to derive an exact distribution. Thus, we try two different transformations to find out which transform is easier to get the exact distribution in terms of calculation complexity. We compare two transformations and comment on the advantages and disadvantages for each transformation.

• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.187-199
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• 2002

When the sample size is small, exact tests are often employed because the asymptotic distribution of the test statistic is in doubt. The advantage of exact tests is that it is guaranteed to bound the type I error probability to the nominal level. In this paper we review the methods of constructing exact tests, the algorithm and commercial software. We also examine the difference between exact p-values obtained from exact tests and true p-values obtained from the true underlying distribution.

• Journal of the Korean Statistical Society
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• v.25 no.4
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• pp.457-469
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• 1996

In this paper, we propose a method for an exact test of H : $p_i$ = $r_i$ for all i against K : $p_i$ $\neq$ $r_i$ for some i in an unbalanced random effect linear model, where $p_i$ denotes the ratio of the i-th variance component to the error variance. Then we present a method to test H : $p_i$ $\leq$ r against K : $p_i$> r for some specific i by applying orthogonal projection on the model. We also show that any test statistic that follows an F-distribution on the boundary of the hypotheses is equal to the one given here.

• Communications for Statistical Applications and Methods
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• v.15 no.3
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• pp.403-409
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• 2008

In this paper we show that the peak of the type I error rate of the Oochran-Armitage trend test could be greater than the nominal level when $2\;{\times}\;C$ contingency tables obtained from two multinomial distributions are extremely unbalanced. This result justifies the use of the exact Cochran-Armitage trend test in extremely unbalanced $2\;{\times}\;C$ contingency tables.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.109-110
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• 2015

This paper presents a computational method that identifies an exact set of optimal overlap rates between critical activities to meet job site specific needs by using rework cost-slope. The procedures to compute the exact solution are provided in peudocode algorithm. The method is coded into Exact Concurrent Construction Scheduling system that allows practitioners to make more informed decision in accordance with the site-specific condition involved in the overlapping of critical activities. Test cases verify the validity of the computational method and the usability of the system.

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.837-846
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• 2012

Monte Carlo methods have been used in exact inference for contingency tables for a long time; however, they suffer from ergodicity and the ability to achieve a desired proportion of valid tables. In this paper, we apply the stochastic approximation Monte Carlo(SAMC; Liang et al., 2007) algorithm, as an adaptive Markov chain Monte Carlo, to the exact test of mutual independence in a multiway contingency table. The performance of SAMC has been investigated on real datasets compared to with existing Markov chain Monte Carlo methods. The numerical results are in favor of the new method in terms of the quality of estimates.

• Journal of the Korea Society of Computer and Information
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• v.23 no.7
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• pp.99-104
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• 2018

In this paper, we compare Fisher's continuous method with an exact discrete analog of Fisher's continuous method from permutation tests for combining p values. The discrete analog of Fisher's continuous method is known to be adequate for combining independent p values from discrete probability distributions. Also permutation tests are widely used as alternatives to conventional parametric tests since these tests are distribution-free, and yield discrete probability distributions and exact p values. In this paper, we obtain permutation p values from discrete probability distributions using Mann-Whitney test data sets (real data and hypothetical data) and combine p values by the exact discrete analog of Fisher's continuous method.

• Communications for Statistical Applications and Methods
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• v.11 no.2
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• pp.413-418
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• 2004

Statistically illiterate people seem to believe that there are some strategies for choosing winning numbers in lottery. One seemingly plausible strategy is to select the hot numbers which most frequently appeared in the past. In this article we investigate the existence of hot numbers in the Korean national lottery called Lotto. A numerical method is proposed to estimate the exact sampling distribution of test statistic for checking the existence of hot numbers among 45 possible numbers of choice.