• The Korean Journal of Applied Statistics
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• v.23 no.3
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• pp.585-594
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• 2010

This study, investigates the effects of dispersion parameters between two response variables in zero-truncated bivariate generalized Poisson distributions. A Monte Carlo study shows that the zero-truncated bivariate Poisson and negative binomial models fit poorly wherein the zero-truncated bivariate count data has heterogeneous dispersion parameters on dependent variables. In addition, we derive the score test for testing the equality of the dispersion parameters and compare its efficiency with the likelihood ratio test.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.339-348
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• 2017

One of the major issues of statistical process control for variables data is monitoring both the mean and the standard deviation. The traditional approach to monitor these parameters is to simultaneously use two seperate control charts. However there have been some works on developing a single chart using a single plotting statistic for joint monitoring, and it is claimed that they are simpler and may be more appealing than the traditonal one from a practical point of view. When using these control charts for variables data, estimating in-control parameters and checking the normality assumption are the very important step. Nonparametric Shewhart-Lepage chart, proposed by Mukherjee and Chakraborti (2012), is an attractive option, because this chart uses only a single control statistic, and does not require the in-control parameters and the underlying continuous distribution. In this paper, we introduce the Shewhart-Lepage chart, and propose the design procedure to find the optimal diagnosis limits when the location and the scale parameters change simultaneously. We also compare the efficiency of the proposed method with that of Mukherjee and Chakraborti (2012).

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.27-36
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• 2005

Finite failure NHPP models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates Per fault. This Paper Proposes reliability model using the generalized gamma distribution, which can capture the monotonic increasing(or monotonic decreasing) nature of the failure occurrence rate per fault. Equations to estimate the parameters of the generalized gamma finite failure NHPP model based on failure data collected in the form of interfailure times are developed. For the sake of proposing shape parameter of the generalized gamma distribution, used to the special pattern. Data set, where the underlying failure process could not be adequately described by the knowing models, which motivated the development of the gamma or Weibull model. Analysis of failure data set for the generalized gamma modell, using arithmetic and Laplace trend tests . goodness-of-fit test, bias tests is presented.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.401-410
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• 2009

Due to the characteristics of 3 ${\times}$ 3 Latin square design which is composed of two block effects and one main effect, powers of rank transformed statistic for testing the main effect are very superior to powers of parametric statistic without regard to the type of population distributions. By order of when all three effects are fixed, when on one block effect is random, when two block effects are random, the rank transform statistic for testing the main effect shows relatively high powers as compared with the parametric statistic. Further when the size of main effect is big with one equivalent size of block effect and the other small size of block effect, powers of rank transformed statistic for testing the main effect demonstrate excellent advantage to powers of parametric statistic.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.931-941
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• 2017

Typical Nonparametric methods for randomized block design with replications are two methods proposed by Mack (1981) and Mack and Skillings (1980). This method is likely to cause information loss because it uses the average of repeated observations instead of each repeated observation in the processing of each block. In order to compensate for this, we proposed a test method using linear placement statistics, which is a score function applied to the joint placement method proposed by Chung and Kim (2007). Monte Carlo simulation study is adapted to compare the power with previous methods.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.567-578
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• 2017

Grouped multivariate data can be tested for differences between two or more groups using multivariate analysis of variance (MANOVA). However, this method cannot be used if several assumptions of MANOVA are violated. In this case, multidimensional scaling (MDS) and analysis of distance (AOD) can be applied to grouped dissimilarities based on the various distances. A permutation test is a non-parametric method that can also be used to test differences between groups. MDS is used to calculate the coordinates of observations from dissimilarities and AOD is useful for finding group structure using the coordinates. In particular, AOD is mathematically associated with MANOVA if using the Euclidean distance when computing dissimilarities. In this paper, we study the between and within group structure by applying MDS and AOD to the grouped dissimilarities. In addition, we propose a new test statistic using the group structure for the permutation test. Finally, we investigate the relationship between AOD and MANOVA from dissimilarities based on the Euclidean distance.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.275-287
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• 1998

The goal of this paper is to suggest the goodness-of-fit test statistic for the no effect model. The comparisons of powers on test statistics are conducted with three types alternative modles.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.605-619
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• 1999

본 논문은 회귀분석에서 오차항의 1차 자기상관 존재 여부 및 그 값을 검정하는 방법을 베이지안 접근법으로 제안하였다. 이 방법은 모수공간의 다중분할로 인해 얻어진 여러 가설들에 대한 다중결정문제를 다중 베이즈요인에 관한 이론과 일반화 Savage-Dickey 밀도비를 이용한 사후확률 추정법을 합성하여 개발되었다. 이 방법은 기존의 검정법들에서 가능한 검정 뿐 아니라 이들이 해결할 수 없는 자기상관에 대한 다중결정문제에도 사용이 가능한데 그 효용성이 있다. 모의실험을 통하여 제안된 검정법의 유효성을 평가하였다.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.555-564
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• 2006

Friedman rank-sum multiple comparison procedure is often applied to nonparametric multiple comparison method under randomized block design. Since this method does not use between-block information, we propose, in this paper, nonparametric multiple comparison procedures employing aligned method suggested by Hedges and Lehmann(1962) under randomized block design. The proposed procedure and Friedman procedure are compared by Monte Carlo simulation study.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1499-1506
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• 2014

Random walk is used for describing random phenomenon in various areas but tests for random walk developed so far are known to suffer from size distortion and low power. Kim et al. (2014) proposed a sign test for unit root (${\rho}=1$) hypothesis based on slopes. This article proposes a Wilcoxon signed rank test based on slopes for unit root hypothesis, and compares it with the augmented Dickey-Fuller test and the sign test by a simulation study. Our results confirm that the nonparametric tests are better than ADF test for small samples like n = 30. The results also show that the sign test is better than the Wilcoxon signed rank test and that for 0 < ${\rho}$ < 1 (-1 < ${\rho}$ < 0), the nonparametric tests suffer from power loss (improvement) as normal error changes to double exponential error.