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Article type: Cover
2008 Volume 30 Issue 8 Pages
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Published: November 01, 2008
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Article type: Appendix
2008 Volume 30 Issue 8 Pages
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Article type: Index
2008 Volume 30 Issue 8 Pages
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Tadamasa KIMURA
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2008 Volume 30 Issue 8 Pages
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Shinji YOKOGAWA
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2008 Volume 30 Issue 8 Pages
644-651
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Kouichi YASUDA, Tadashi SHIOTA, Yohtaro MATSUO
Article type: Article
2008 Volume 30 Issue 8 Pages
652-659
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Shigeharu YAMAMOTO, Yuichi AOKI, Hirokazu TANAKA
Article type: Article
2008 Volume 30 Issue 8 Pages
660-669
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Sadanori ITO
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2008 Volume 30 Issue 8 Pages
670-677
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Kiyoshi NIKAWA
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2008 Volume 30 Issue 8 Pages
678-685
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Akira MOTOYAMA, Toshinari MATSUOKA
Article type: Article
2008 Volume 30 Issue 8 Pages
686-691
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Sigeo UMEZAKI, Shoken SHIMIZU, Kyouko HAMAJIMA
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2008 Volume 30 Issue 8 Pages
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Kiyoshi TAKAHISA
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2008 Volume 30 Issue 8 Pages
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Yukio KITAMURA
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2008 Volume 30 Issue 8 Pages
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Tadashi MURATA
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2008 Volume 30 Issue 8 Pages
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Article type: Appendix
2008 Volume 30 Issue 8 Pages
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Article type: Appendix
2008 Volume 30 Issue 8 Pages
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Hideki NAGATSUKA, Hisashi YAMAMOTO, Toshinari KAMAKURA
Article type: Article
2008 Volume 30 Issue 8 Pages
713-726
Published: November 01, 2008
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The gamma distribution, having location (threshold), scale and shape parameters, is used as a model for distributions of life spans, reaction time, and for other types of non-symmetrical data. It has been said that the inference for the three-parameter gamma distribution is difficult because of nonregularity in maximum likelihood estimation although numerous papers have appeared over the years. It is usual to avoid fitting the three-parameter gamma distribution and to fit the two-parameter gamma distribution to data in practice. The fitting, however, becomes poor if the threshold exists. In this article, for avoiding this problem, we propose a new estimator of the shape parameter of the gamma distribution, which does not depend on the location parameter. A simulation study shows that the proposed estimator performs better than the Nagatsuka et al. (2006) estimator when the threshold exists even though that is close to zero. The proposed method is also illustrated by an example of a maximum flood level data.
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Article type: Appendix
2008 Volume 30 Issue 8 Pages
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Article type: Index
2008 Volume 30 Issue 8 Pages
728-732
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Article type: Appendix
2008 Volume 30 Issue 8 Pages
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Article type: Appendix
2008 Volume 30 Issue 8 Pages
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Article type: Appendix
2008 Volume 30 Issue 8 Pages
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Article type: Cover
2008 Volume 30 Issue 8 Pages
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Published: November 01, 2008
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