• 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.24 no.1
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• pp.95-103
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• 2013

Nonparametric procedure in randomized block design (RBD) was proposed by Friedman (1937) for general alternatives. Also Page (1963) suggested the test for ordered alternatives in RBD. In this paper, we proposed the new nonparametric method in randomized block design using aligned method suggested by Hodges and Lehmann (1962) and the joint placement described in Chung and Kim (2007). Also, Monte Carlo simulation study was adapted to compare the power of the proposed procedure with those of previous procedure.

• Journal of the military operations research society of Korea
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• v.27 no.2
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• pp.124-133
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• 2001

The purpose of this study is investigate the differences among parametric and nonparametric test statistics for the tree alternative hypothesis in the randomized block design. As the results, it was found that there was no large differences among parametric and nonparametric test statistics in power when the block sizes were larger, and Hollander's statistic had better power than other nonparametric test statistics. It is recommended that Hollander's test statistic is more useful method when we have no information about the distribution of population.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1105-1112
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• 2011

Mack (1981), Skilling and Wolfe (1977, 1978) proposed typical nonparametric method in randomized block design with replications. In this paper, we proposed the procedures based on placement as extension of the two sample placement tests described in Orban and Wolfe (1982) and treatment versus control tests described in Kim (1999). Also Monte Carlo simulation study is adapted to compare power of the proposed procedure with those of previous procedures.

• The Korean Journal of Applied Statistics
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• v.31 no.5
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• pp.599-610
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• 2018

The method of Mack and Skillings (Technometrics, 23, 171-177, 1981) is a nonparametric multiple comparison method in a randomized block design with replications. This method is likely to result in loss of information because each block is ranked using the average of observations instead of repeated observations. In this paper, we proposed a new nonparametric multiple comparison method in the randomized block model with replications using an alignment method proposed by Hodges and Lehmann (The Annals of Mathematical Statistics, 33, 482-497, 1962) that extend the joint placement method proposed by Chung and Kim (Communications for Statistical Applications and Methods, 14, 551-560, 2007). In addition, Monte Carlo simulation compared the family wise error rate and power with the parametric method and the nonparametric method.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.281-290
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• 2017

Mack and Skillings (1980) proposed a nonparametric method in a randomized block design with replications. This method employs the mean of observations instead of each observation. However, it has the inherent disadvantage that there may be a loss of information. In this paper, we proposed a nonparametric method that employees an aligned method and linear placement statistics to supplement its weakness. A Monte-Carlo study is performed to compare the power of the proposed method with previous methods.

• The Korean Journal of Applied Statistics
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• v.10 no.1
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• pp.15-27
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• 1997

Randomized block design (RBD) with three replication is very frequently adopted in agricultural experiments of the Rural Development Administration of Korea. Even though it works well in field trials of traditional crops, it may not accomodate trial site conditions and/or experimental environment. In this research report, we deal with two such cases. The first case is for a crop experiment in green houses. In house conditions, RBD may not be appropriate since it cannot reflect two directions of the yield gradient. So, a Latin square design is suggested as an alternative. The second case is for local field experiments of the newly-inbred rice. RBD with three replications is used without doubt for decades, even though the site layout is not appropriately shaped for the design. In this case, we suggest the RBD in two blocks with multiple replicates for control varieties as an alternative. To improve the quality of statistical experimental designs in over one-thousand agricultural trials performed annually in the Rural Development Administration, we need to re-train agricultural researchers on the design and analysis of experiments and call for concerns of Korean statisticians.

• The Korean Journal of Applied Statistics
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• v.9 no.1
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• pp.139-152
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• 1996

In this paper we propose nonparametric tests using optimal weights for umbrella alternatives in a randomized block design. We obtain the optimal weights by maximizing the asymptotic relative efficiency of the proposed test statistics with respect to Mack and Wolf(1981) type test statistic, and investigate asymptotic relative efficiencies of the proposed test statistics using these optimal weights relative to Mack and Wolfe type statistics and linear rank statistic. Throughout simulations for small samples, the proposed test statistic has good powers rather than the other two tests when the block sizes are different.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.423-433
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• 1998

In agricultural field experiments, the completely randomized block design is often used for the analysis of variety trials. An important assumption is that every experimental unit in each block has the some fertility. But, in most agricultural field experiments there often exists a systematic heterogeneity in fertility among the experimental units. To account for the heterogeneity, we propose to use the hierarchical generalized linear models. We compare our analysis of the data from Scottish Agricultural colleges list with that using Markov chain Monte Carlo method.