• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.35-43
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• 2011

In this paper, we introduce a unit root test via discrete cosine transform in the AR(1) process. We first investigate the statistical properties of DCT coefficients under the stationary AR(1) process and the random walk process in order to verify the validity of the proposed method. A bootstrapping approach is proposed to induce the distribution of the test statistic under the unit root. We performed simulation studies for comparing the powers of the Dickey-Fuller test and the proposed test.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.159-167
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• 2017

Observations identified as potential outliers are usually tested for real outliers; however, some outlier detection methods skip a formal test or perform a test using simulated p-values. We introduce test procedures for outliers by testing subsets of potential outliers rather than by testing individual observations of potential outliers to avoid masking or swamping effects. Examples to illustrate methods and a Monte Carlo study to compare the power of the various methods are presented.

• Communications for Statistical Applications and Methods
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• v.17 no.5
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• pp.667-678
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• 2010

It is important to detect the gene-gene interaction in GWAS(Genome-Wide Association Study). There are many studies about detecting gene-gene interaction. The one is Multifactor dimensionality reduction method. But MDR method is not applied continuous data and expanded multifactor dimensionality reduction(E-MDR) method is suggested. The goal of this study is to evaluate the power of E-MDR for identifying gene-gene interaction by simulation. Also we applied the method on the identify interaction e ects of single nucleotid polymorphisms(SNPs) responsible for economic traits in a Korean cattle population (real data).

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.699-706
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• 2016

Outlier detection methods without performing a test often do not succeed in detecting multiple outliers because they are structurally vulnerable to a masking effect or a swamping effect. This paper considers testing procedures supplemented to a clustering-based method of identifying the group with a minority of the observations as outliers. One of general steps is performing a variety of t-test on individual outlier-candidates. This paper proposes a sequential procedure for searching for outliers by changing cutoff values on a cluster tree and performing a test on a set of outlier-candidates. The proposed method is illustrated and compared to existing methods by an example and Monte Carlo studies.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.301-309
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• 2017

It is often possible to test for differences in population means when two or more samples are extracted from each N population. However, it is not possible to test for the mean difference if one sample is extracted from each population since a sample mean does not exist. But, by dividing a group of samples extracted one by one into two groups and generating a sample mean, we can identify a heterogeneity that may exist within the group by comparing the differences of the groups' mean. Therefore, we propose a minimum combination t-test method that can test the mean difference by the number of combinations that can be divided into two groups. In this paper, we proposed a method to test differences between means to check heterogeneity in a group of extracted samples. We verified the performance of the method by simulation study and obtained the results through real data analysis.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.753-761
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• 2019

A test procedure based on a Kendall's τ statistic is proposed for the association of bivariate interval censored data. In particular, a leverage bootstrap technique is applied to replace unknown failure times and a classical adjustment method is applied for treating tied observations. The suggested method shows desirable results in simulation studies. An AIDS dataset is analyzed with the suggested method.

• The Korean Journal of Applied Statistics
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• v.2 no.1
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• pp.35-47
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• 1989

This article deals with the method of ML among the methods of estimating m gene frequenecies in the Generalized ABO-like Blood Group Systems and with the statistical testing about the differencies of gene frequencies by using these estimators. Especially, the generalization about the Homogeneity testing problem is tried and thus it enables us to test of Homogeneity of m gene frequencies. Finally, in the example, ML estimator is compared with other estimators suggested by Bernstein method, by adjusted Bernstein method and by modified Bernstein method, and statistical testing in the above is carried out by using orthogonal partitioning.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1401-1408
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• 2013

Kim and Kim (1992) proposed typical nonparametric method for umbrella alternative in randomized block design with replications. In this paper, We consider a test procedure for umbrella alternatives in a randomized block design using extension of the two sample placement tests described in Orban and Wolfe (1982) and treatment tests described in Kim (1999). We perform a Monte Carlo study to compare the empirical powers of the test statistics for underlying distributions.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.309-314
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• 2016

본 연구에서는 확률강우량 산정을 위한 빈도해석 시 보다 타당하고, 신뢰도 높은 강우자료를 선정하여 수공구조물 설계 및 재난방재계획에 합리적인 기준을 제시하고자 이상치 적용에 따른 확률강우량을 산정 비교하였다. 이상치검정의 비교를 위하여 Grubbs and Beck 방법과 미국 수자원평의회 방법을 적용하였으며, 적용결과에 따라 강우자료를 조정하여 확률강우량을 산정 비교하여 타당성을 검토하였다. 본 연구의 수행으로 이상치검정에 따라 강우자료 년수에 변화가 있었으며, 기존의 방법과 확률강우량을 비교하였을 때, 증감이 나타났다.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.241-251
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• 2018

The analysis of gene sets in microarray has advantages in interpreting biological functions and increasing statistical powers. Many statistical methods have been proposed for detecting significant gene sets that show relations between genes and phenotypes, but there is no consensus about which is the best to perform gene sets analysis and permutation based tests are considered as standard tools. When many gene sets are tested simultaneously, a large number of random permutations are needed for multiple testing with a high computational cost. In this paper, several parametric approximations are considered as alternatives of the permutation distribution and the moment based gene set test has shown the best performance for providing p-values of the permutation test closely and quickly on a general framework.