Bulletin of the Computational Statistics of Japan
Online ISSN : 2189-9789
Print ISSN : 0914-8930
ISSN-L : 0914-8930
Volume 22, Issue 2
Displaying 1-15 of 15 articles from this issue
  • Article type: Cover
    2010 Volume 22 Issue 2 Pages Cover1-
    Published: May 12, 2010
    Released on J-STAGE: May 01, 2017
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  • Article type: Appendix
    2010 Volume 22 Issue 2 Pages App1-
    Published: May 12, 2010
    Released on J-STAGE: May 01, 2017
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  • Article type: Index
    2010 Volume 22 Issue 2 Pages i-
    Published: May 12, 2010
    Released on J-STAGE: May 01, 2017
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  • Article type: Index
    2010 Volume 22 Issue 2 Pages ii-
    Published: May 12, 2010
    Released on J-STAGE: May 01, 2017
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  • Kotoe Katayama, Hiroyuki Minami, Masahiro Mizuta
    Article type: Article
    2010 Volume 22 Issue 2 Pages 83-89
    Published: May 12, 2010
    Released on J-STAGE: May 01, 2017
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    We propose a hierarchical clustering in the framework of Symbolic Data Analysis (SDA). SDA was proposed by Diday at the end of the 1980s and is a new approach for analysing huge and complex data. In SDA, an observation is described by not only numerical values but also "higher-level units"; sets, intervals, distributions, etc. Most SDA works have dealt with only intervals as the descriptions. In this paper, we propose a hierarchical clustering for distribution valued data and show its effectiveness through a numerical simulation.
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  • Xueyan Zhao, Yutaka Tanaka
    Article type: Article
    2010 Volume 22 Issue 2 Pages 91-108
    Published: May 12, 2010
    Released on J-STAGE: May 01, 2017
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    The present paper discussed two related problems, i.e., 1) Do the so-called horseshoe effects exist in Hayashi's second method of quantification or Quantification II? If the answer is yes, how do they appear? 2) How can we avoid the influence of the horseshoe effects in Quantification II? As it was found that horseshoe effects existed, we proposed two-step methods of linear discriminant analysis after unidimensional scaling. For unidimensional scaling we proposed two different types of partial canonical correlation analysis or partial correspondence analysis suitable for the cases in which predictor variables were mutually highly correlated and were only slightly correlated. Then we applied Quantification II and the two-step methods to artificial data sets in which predictor variables had unidimensional structure and compared the performances with correct classification rates. The results for the training data sets showed that Quantification II was superior to the two-step methods when the sample size N was small but the result of comparison was opposite when N was large and that the change point of the sample size increased as K increased, where K indicates the number of categories of categorized predictor variables. The results for the test data sets showed that the two-step method was superior for all N. Considering the situations where test data were not available, we studied whether 0.632 bootstrap estimation could be used for estimating the comparative performances for the test data sets and found that the bootstrap estimation was useful for choosing the method with higher performance.
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  • Takashi Nagakubo, Masashi Goto
    Article type: Article
    2010 Volume 22 Issue 2 Pages 109-129
    Published: May 12, 2010
    Released on J-STAGE: May 01, 2017
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    The data measured repeatedly for same individual over time is called longitudinal data. In longitudinal data analysis, if response is continuous and normally distributed, repeated measures ANOVA is often used. But assumption required of normality is not always satisfied and validity of parametric approarch as repeated measures ANOVA is suspected. We propose rank experimental distribution method that is distribution-free to relax restriction of parametric apporoach. In case study, rank experimental distribution method and repeated measures ANOVA were different in the outcome. It is considered to be due to a difference in the underlying distribution. Then, we conducted the simulation that is supposed underlying distribution is normal or skewed to investigate whether for group effect, time effect and interaction the power of two method is different. As a result of the simulation, for group effect, time effect and interaction the power of both methods is almost the same in normally distributed data. And for group effect, time effect and interaction the power of rank experimental distribution method is higher than repeated measures ANOVA. So we have showed the rank experimental distribution method is useful for longitudinal data analysis.
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  • Makoto Tomita, Toshiharu Fujita, Kei Kamide, Hironori Hanada, Toshiyuk ...
    Article type: Article
    2010 Volume 22 Issue 2 Pages 131-142
    Published: May 12, 2010
    Released on J-STAGE: May 01, 2017
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    Recently, we want to know not only DNA sequences but also a relationship between genotype and phenotype in genomic data. There are the very large scale of genotype data, we have to use the Linux OS environment for an analysis of statistical genetics. However, there are several R packages which are available for these analyses. Then, in this paper, we introduce these several applications for genome-wide association analyses, and we showed results of an association analysis for real data.
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  • Tomokazu Fujino
    Article type: Article
    2010 Volume 22 Issue 2 Pages 143-152
    Published: May 12, 2010
    Released on J-STAGE: May 01, 2017
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  • Xiaoling Dou
    Article type: Article
    2010 Volume 22 Issue 2 Pages 153-154
    Published: May 12, 2010
    Released on J-STAGE: May 01, 2017
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  • Article type: Appendix
    2010 Volume 22 Issue 2 Pages 155-
    Published: May 12, 2010
    Released on J-STAGE: May 01, 2017
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  • Article type: Appendix
    2010 Volume 22 Issue 2 Pages 156-
    Published: May 12, 2010
    Released on J-STAGE: May 01, 2017
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  • Article type: Appendix
    2010 Volume 22 Issue 2 Pages 157-
    Published: May 12, 2010
    Released on J-STAGE: May 01, 2017
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  • Article type: Appendix
    2010 Volume 22 Issue 2 Pages App2-
    Published: May 12, 2010
    Released on J-STAGE: May 01, 2017
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  • Article type: Cover
    2010 Volume 22 Issue 2 Pages Cover2-
    Published: May 12, 2010
    Released on J-STAGE: May 01, 2017
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