Bulletin of the Computational Statistics of Japan
Online ISSN : 2189-9789
Print ISSN : 0914-8930
ISSN-L : 0914-8930
Volume 22, Issue 1
Displaying 1-16 of 16 articles from this issue
  • Article type: Cover
    2010 Volume 22 Issue 1 Pages Cover1-
    Published: January 31, 2010
    Released on J-STAGE: May 01, 2017
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  • Article type: Appendix
    2010 Volume 22 Issue 1 Pages App1-
    Published: January 31, 2010
    Released on J-STAGE: May 01, 2017
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  • Article type: Index
    2010 Volume 22 Issue 1 Pages i-
    Published: January 31, 2010
    Released on J-STAGE: May 01, 2017
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  • Article type: Index
    2010 Volume 22 Issue 1 Pages ii-
    Published: January 31, 2010
    Released on J-STAGE: May 01, 2017
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  • Hiroshi Yamashita
    Article type: Article
    2010 Volume 22 Issue 1 Pages 1-2
    Published: January 31, 2010
    Released on J-STAGE: May 01, 2017
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  • Toshio Shimokawa, Masashi Goto
    Article type: Article
    2010 Volume 22 Issue 1 Pages 3-21
    Published: January 31, 2010
    Released on J-STAGE: May 01, 2017
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    In survival analysis, the useful tool for exploration of the factors is tree structured method. Eto et al. (2007) have propose the multi-split tree structured method based on k-samples generalized rank test statistics (MUSTGRAS). However, these nonparametric approaches do not have consistency in data analysis process. Then, we proposed the methodology of data-adaptive multi-split tree structured method (DAMUST), assuming the power-normal distribution as the survival distribution of each terminal node, where power-normal distribution is defined as the distribution specified before the power-normal transformation. We evaluated the performance of the DAMUST by some practical examples with survival data and small scale simulation. As a result, DAMUST has better performance than MUSTGRAS. On the other hand, we can evaluate the survival distribution for each terminal nodes based on the power normal distribution using DAMUST by way of simulation, clinical study, etc. Consequently, DAMUST is better useful method than MUSTGRAS.
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  • Keiji Nishijima, Toshinari Kamakura
    Article type: Article
    2010 Volume 22 Issue 1 Pages 23-35
    Published: January 31, 2010
    Released on J-STAGE: May 01, 2017
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    We investigate a null distribution of the test statistics when the counting process of recurrent events is modeled as a homogenous Poisson process. We give lower bound and upper bound theoretically and consider to approximate the null distribution by linear combinations of lower bound and upper bound, since it is difficult to derive exact distribution of the test statistics which is a distribution of maximum value of dependent chi-square distributions with 1 degrees of freedom (χ^2_1 distribution) in case of n-recurrent events. The lower bound and the upper bound are given by the distribution of a maximum value of independent n-2 χ^2_1 distributions and χ^2_1 distribution, respectively. Furthermore, we investigate approximation by the empirical distribution since the approximations using the bounds cannot provide good approximation of the null distribution. Our simulation study suggests that the null distribution of the test statistics can be well approximated by gamma distribution corresponding to the number of events over the entire region. The regression formula is also developed to determine the parameters of gamma distribution corresponding to the number of events. Our study shows that the gamma distribution is more appropriate approximation than the approximation using the bounds since it could be possible to keep significant level as a nominal level in the hypothesis testing.
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  • Shuichi Shinmura
    Article type: Article
    2010 Volume 22 Issue 1 Pages 37-57
    Published: January 31, 2010
    Released on J-STAGE: May 01, 2017
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    Revised IP-OLDF (optimal linear discriminant function using integer programming) is a linear discriminant function used to obtain the minimum number of misclassifications (MNM) of training data using integer programming. However, integer programming requires a large computation time. Therefore, we aim to reduce the computation time using linear programming. In the first phase, Revised LP-OLDF (optimal linear discriminant function using linear programming) is applied to the data, and the data are categorized into two groups: those classified correctly and those not classified by support vectors. In the second phase, Revised IP-OLDF is applied to the misclassified data. This method is called Revised IPLP-OLDF. In this sturdy, we devise a method to reduce the computation time by using Revised IPLP-OLDF. In addition, it is evaluated whether the number of misclassifications obtained using Revised IPLP-OLDF is equals to the MNM. Four types of real data, namely, data pertaining to students, irises, Swiss bank-notes, and medicine, are used as training data. Four types of re-sampling data generated from the real data are used as the evaluation data. In these evaluation data, there are a total of 149 models for all combinations of explanatory variables. The numbers of misclassifications and calculation times of the 149 models are compared using Revised IPLP-OLDF and Revised IP-OLDF. The following results are obtained: 1. Revised IPLP-OLDF considerably reduces the computation time. 2. In the case of training data, the number of misclassifications obtained for all 149 models using Revised IPLP-OLDF is equal to the MNM obtained using Revised IP-OLDF. 3. In the case of the evaluation data, for most of the models, the numbers of misclassifications obtained using Revised IPLP-OLDF is equal to that obtained using Revised IP-OLDF. 4. It is concluded that the generalization abilities of both methods are high, because the difference between the probability of misclassifications of training and evaluation data is almost within 2%. Therefore, it is concluded that Revised IPLP-OLDF can be applied to real problems in lieu of Revised IP-OLDF.
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  • Yoshihiro Matsubara, Wataru Sakamoto
    Article type: Article
    2010 Volume 22 Issue 1 Pages 59-71
    Published: January 31, 2010
    Released on J-STAGE: May 01, 2017
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  • Article type: Bibliography
    2010 Volume 22 Issue 1 Pages 73-75
    Published: January 31, 2010
    Released on J-STAGE: May 01, 2017
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  • Kazuma Takahashi
    Article type: Article
    2010 Volume 22 Issue 1 Pages 77-78
    Published: January 31, 2010
    Released on J-STAGE: May 01, 2017
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  • Article type: Appendix
    2010 Volume 22 Issue 1 Pages 79-
    Published: January 31, 2010
    Released on J-STAGE: May 01, 2017
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  • Article type: Appendix
    2010 Volume 22 Issue 1 Pages 80-
    Published: January 31, 2010
    Released on J-STAGE: May 01, 2017
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  • Article type: Appendix
    2010 Volume 22 Issue 1 Pages 81-
    Published: January 31, 2010
    Released on J-STAGE: May 01, 2017
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  • Article type: Appendix
    2010 Volume 22 Issue 1 Pages App2-
    Published: January 31, 2010
    Released on J-STAGE: May 01, 2017
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  • Article type: Cover
    2010 Volume 22 Issue 1 Pages Cover2-
    Published: January 31, 2010
    Released on J-STAGE: May 01, 2017
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