• Title/Summary/Keyword: Randomized block design

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Nonparametric Multiple Comparison Procedure Using Alignment Method Under Randomized Block Design (랜덤화 블록 모형에서 정렬 방법을 이용한 비모수 다중비교법)

  • Han, Ji-Ung;Kim, Dong-Jae
    • 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.

Nonparametric procedures using aligned method and joint placement in randomized block design (랜덤화 블록 계획법에서 정렬방법과 결합 위치를 이용한 비모수 검정법)

  • Jo, Sungdong;Kim, Dongjae
    • 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.

The Analysis of power of the Test Statistics for the Randomized Block Design (확률화 블록 실험계획 모형에서 검정 통계량들의 검정력 분석)

  • 배현웅;김제영
    • 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.

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Nonparametric method using placement in a randomized complete block design (랜덤화 블록 계획법에서 위치를 이용한 비모수 검정법)

  • Sim, Sujin;Kim, Dongjae
    • 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.

Nonparametric procedures using placement in randomized block design with replications (반복이 있는 랜덤화 블록 계획법의 위치를 이용한 비모수 검정법)

  • Lee, Sang-Yi;Kim, Dong-Jae
    • 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.

Nonparametric multiple comparison method using aligned method and joint placement in randomized block design with replications (반복이 있는 랜덤화 블록 모형에서 정렬방법과 결합위치를 이용한 비모수 다중비교법)

  • Hwang, Juwon;Kim, Dongjae
    • 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.

Nonparametric method using aligned method and linear placement statistics in randomized block design with replications (반복이 있는 랜덤화블록 모형에서 정렬방법과 선형위치통계량을 이용한 비모수 검정법)

  • Jeon, Soyoung;Kim, Dongjae
    • 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.

Alternation to the Randomized Block Design for Agricultural Experiments in Korea (농업실험에서 임의화블록설계에 대한 대안 - 농촌진흥청 사례들을 중심으로 -)

  • 허명회;한원식;신한풍
    • 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.

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Nonparametric tests using optimal weights for umbrella alternatives in a randomized block design (확률화 블럭 계획법에서 최적 가중치를 이용한 우산형 대립가설의 비모수검정법)

  • 김동희;김영철
    • 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.

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Modelling Heterogeneity in Fertility for Analysis of Variety Trials (밭의 비옥도를 고려한 품종실험 분석)

  • 윤성철;강위창;이영조;임용빈
    • 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.

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