• Communications for Statistical Applications and Methods
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• 제3권2호
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• pp.197-204
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• 1996

We consider a two sample rank test for a bivariate mixture distribution based on Johnson's quantile score. The test statistic is simple to calculate and the exact distribution under the null hypothesis is obtained. A numerical example is given.

• Communications for Statistical Applications and Methods
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• 제18권6호
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• pp.707-716
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• 2011

분위수와 분위(또는 타점위치)는 학계에서나 산업계에서 널리 사용되고 있다. 그런데 통계 소프트웨어에 구현되어 있는 표본 분위수 계산방법들과 표본 분위 계산방법들은 각각 적어도 일곱 가지가 있다. 분위수들이나 분위들을 정의하는 방법들 간의 사소해 보이는 차이가 그 값을 토대로 이루어지는 결정의 큰 차이를 가져올 수 있다. 이 논문에서는 경험적 누적확률을 사용한 기본 타점위치 방법과 Blom (1958)의 제안을 토대로 파생된 여섯 가지 타점위치 방법의 특징과 차이점을 논의하였다. 또 통계소프트웨어에 구현되어 있는 일곱 가지 표본분위수 계산방법들의 특징과 차이점들을 논의한 후 이들을 망라하는 하나의 일반식을 제시하였다. 이 논문에서는 이 일반식으로부터 얻어지는 통찰을 토대로 표본분위수에 대응되는 표본분위를 구하는 방법을 제안하였고, 이 제안을 각 표본분위수 방법에 적용하여 대응되는 표본분위 방법을 도출하였다. 이런 대응관계는 표본분위수와 표본분위에 대한 종합적 이해와 적용에 도움을 줄 수 있을 것이다.

• Communications for Statistical Applications and Methods
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• 제28권6호
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• pp.643-653
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• 2021

Few studies are found in literature on estimation of population quantiles using the method of ranked set sampling (RSS). The optimal RSS strategy is to select observations with at most two fixed rank order statistics from different ranked sets. In this paper, a near optimal unbalanced RSS model for estimating p^th(0 < p < 1) population quantile is proposed. Main advantage of this model is to use each rank order statistics and is distributionfree. The asymptotic relative efficiency (ARE) for balanced RSS, unbalanced optimal and proposed near-optimal methods are computed for different values of p. We also compared these AREs with respect to simple random sampling. The results show that proposed unbalanced RSS performs uniformly better than balanced RSS for all set sizes and is very close to the optimal RSS for large set sizes. For the practical utility, the near optimal unbalanced RSS is recommended for estimating the quantiles.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.160-160
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• 2023

Generally, Global Climate Models (GCM) cannot be used directly due to their inherent error arising from over or under-estimation of climate variables compared to the observed data. Several bias correction methods have been devised to solve this problem. Most of the traditional bias correction methods are one dimensional as they bias correct the climate variables separately. One such method is the Quantile Mapping method which builds a transfer function based on the statistical differences between the GCM and observed variables. Laux et al. introduced a copula-based method that bias corrects simulated climate data by employing not one but two different climate variables simultaneously and essentially extends the traditional one dimensional method into two dimensions. but it has some limitations. This study uses objective functions to address specifically, the limitations of Laux's methods on the Quantile Mapping method. The objective functions used were the observed rank correlation function, the observed moment function and the observed likelihood function. To illustrate the performance of this method, it is applied to ten GCMs for 20 stations in South Korea. The marginal distributions used were the Weibull, Gamma, Lognormal, Logistic and the Gumbel distributions. The tested copula family include most Archimedean copula families. Five performance metrics are used to evaluate the efficiency of this method, the Mean Square Error, Root Mean Square Error, Kolmogorov-Smirnov test, Percent Bias, Nash-Sutcliffe Efficiency and the Kullback Leibler Divergence. The results showed a significant improvement of Laux's method especially when maximizing the observed rank correlation function and when maximizing a combination of the observed rank correlation and observed moments functions for all GCMs in the validation period.

• Journal of the Korean Data and Information Science Society
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• 제18권4호
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• pp.1005-1021
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• 2007

Traditional procedures for DNA microarray data analysis are to preprocess and normalize the gene expression data, and then to analyze the normalized data using statistical tests. Drawbacks of the traditional methods are: genuine biological signal may be unwillingly eliminated together with artifacts, the limited number of arrays per gene make statistical tests difficult to use the normality assumption or nonparametric method, and genes are tested independently without consideration of interrelationships among genes. A novel method using average rank in each array is proposed to eliminate such drawbacks. This average rank method monitors differentially regulated genes among genetically different groups and the selected genes are somewhat different from those selected by traditional P-value method. Addition of genes selected by the average rank method to the traditional method will provide better understanding of genetic differences of groups.

• Journal of Information Technology Applications and Management
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• 제25권1호
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• pp.67-85
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• 2018

We propose a noise reduced risk aversion index for measuring risk aversion through a laboratory experiment to overcome disadvantages of the multiple pricing list format developed by Holt and Laury (2002). We use randomized multiple list choices with coarser classification and reward weighting, supplement the rank of risk aversion with extra individual characteristics of risk attitude, and construct an index of risk aversion by standardizing the risk aversion ranking with quantile normalization. Our method reduces multiple switching problems that noisy decision makers mistakenly commit in experimental approaches, so that it is free of the framing effect which severely occurred in the HL. Furthermore, the index doesn't utilize any specific utility function or probability weighting, which allows researcher to hold the independence axiom. Since our noise reduced index of risk aversion has many good traits, it is widely used and applied to reveal fundamental characteristics of risk-related behaviors in economics and finance regardless of experimental environment.

• 응용통계연구
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• 제26권2호
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• pp.307-319
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• 2013

마이크로어레이 실험의 특성상 표본의 수가 많지 않는 단점을 보완하고 분석 결과를 일반화하기 위하여 공개 저장소에 축적된 자료 중에 연구 목적이 동일한 여러 연구들을 통합하여 분석하려는 시도가 활발하다. 그러나 실험에서 사용한 플랫폼이 서로 다른 경우에는 유전자 관찰값의 분포가 달라지기 때문에 통합이 어렵고 최상의 통합 방법이 제시되어 있지 않다. 본 논문에서는 순위 기반 중위수, 분위수 이산화와 표준화를 각각 이용하여 변환한 자료값을 직접 합치거나 메타분석을 하여 연구 결과를 합치는 방법을 알아 보았다. 또한 GEO에서 다운받은 실제 자료들을 이용하여 네 가지 방법의 장단점과 효과를 비교하였고 서로 다른 연구 자료를 통합하는 것의 영향을 알아보았다.

• Communications for Statistical Applications and Methods
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• 제23권5호
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• pp.363-383
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• 2016

The Weibull family of lifetime distributions play a fundamental role in reliability engineering and life testing problems. This paper investigates the potential usefulness of transmuted new generalized Weibull (TNGW) distribution for modeling lifetime data. This distribution is an important competitive model that contains twenty-three lifetime distributions as special cases. We can obtain the TNGW distribution using the quadratic rank transmutation map (QRTM) technique. We derive the analytical shapes of the density and hazard functions for graphical illustrations. In addition, we explore some mathematical properties of the TNGW model including expressions for the quantile function, moments, entropies, mean deviation, Bonferroni and Lorenz curves and the moments of order statistics. The method of maximum likelihood is used to estimate the model parameters. Finally the applicability of the TNGW model is presented using nicotine in cigarettes data for illustration.