• 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 Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.333-341
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• 1997

The purpose of this paper is to compare the manufacturing process with random covariate using multiple comparison procedure. The methodology that compares each manufacturing process by inspecting the number of nonconforming items out of k-treatment, has serveral limitations and problems according to the method and contect of the analysis. The proper way of analysis, therefore, could be obtained by the multiple comparison procedure of simultaneous confidence region of variance components. Effections that affect a manufactuing process may be predictive of responce to treatments are called covariates. In the study of comparing several treatments, prsense of covariate may bias the estimates of treatment effects.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.233-244
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• 2006

In this paper, we develop the Bayesian multiple comparisons procedure for the binomial distribution. We suggest the Bayesian procedure based on fractional Bayes factor when noninformative priors are applied for the parameters. An example is illustrated for the proposed method. For this example, the suggested method is straightforward for specifying distributionally and to implement computationally, with output readily adapted for required comparison. Also, some simulation was performed.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.229-236
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• 2004

This article presents a multiple comparison ranking procedure for several products of the Poisson rates. A preference probability matrix that warrants the optimal comparison ranking is introduced. Using a Bayesian Monte Carlo method, we develop simulation-based procedure to estimate the matrix and obtain the optimal ranking via a row-sum scores method. Necessary theory and two illustrative examples are provided.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.843-850
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• 2006

In this paper, we consider the Bayesian multiple comparisons problem for K bivariate exponential populations to make inferences on the relationships among the parameters based on observations. And we suggest the Bayesian procedure based on fractional Bayes factor when noninformative priors are applied for the parameters. Also, we give a numerical examples to illustrate our procedure.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.1
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• pp.42-49
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• 2002

In this paper, we develop the Bayesian multiple comparison procedure for the normal model. The procedure which we suggest is based on the fractional Bayes factor of O'Hagan (1995). We apply our procedure to normal populations, when noninformative prior is assumed to the model parameters. We derive explicit form of Bayes Factors when the number of populations is greater than 3. A famous data is analyzed by the proposed procedure. For this example, the suggested method is straightforward for specifying distributionally and to implement computationally, with output readily adapted for required comparison.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.775-785
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• 1997

Robustness rates for repeated measures pairwise multiple comparison procedures were investigated in a split plot design with one between- and one within-subjects factor using untrimmed and trimmed data. Five factors were manipulated in the study: distribution, sphericity, variance-covariance heteroscedasticity, total sample size, and sample size ratio. The Welsch test (W) and the Welsch test on trimmed data $(W_{RT})$ performed better than the other procedures, but had a liberal tendency. The trimmed difference score Bonferroni Procedure $(B_{DT})$ was a good choice in some conditions.

• 한국데이터정보과학회:학술대회논문집
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• 2001.10a
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• pp.67-75
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• 2001

A completely randomized growth curve model was defined by Zerbe(1979). We propose the fully significant difference procedure for multiple comparisons of completely randomized growth curve model. The standard F test is useful tool to multiple comparisons of the completely randomized growth curve model. The proposed method is applied to experimental data.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.1027-1036
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• 2017

Multiple comparisons are required to confirm whether or not something is significant if the null hypothesis to test whether the difference between more than three treatments is rejected in a one-way layout. There are both parametric multiple comparison method Tukey (1953) and Nonparametric multiple comparison method based on Kruskal-Wallis (1952).This procedure is applied to a mixed sample of all data and then an average ranking is used for each of three or more treatments. In this paper, a new nonparametric multiple comparison procedure based on joint placements for a one-way layout as extension of the joint placements described in Chung and Kim (2007) was proposed. Monte Carlo simulation is also adapted to compare the family wise error rate (FWE) and the power of the proposed method with previous methods.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.39-47
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• 2006

The Bayesian approach to multiple testing procedure for one sample testing problem proposed by Scott and Berger (2003) is extended to two-sample comparison problem in microarray experiments. The prior distribution of each gene's mean for one sample is given conditionally on the corresponding gene's mean for the other sample. Posterior distributions of interesting parameters are derived and estimated based on an importance sampling method. A simulated example is given for illustration.