| Budget Amount *help |
¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2015: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2014: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2013: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Outline of Final Research Achievements |
In this research we consider the following problems.
First, in Bayesian estimation, it is very important to choose an objective prior distribution when little prior information is available. In this research we derived a non-informative prior which maximizes the alpha divergence between the prior and the corresponding posterior distribution for non-regular family of distributions whose support depends on unknown parameter. Secondly, lower bounds for the Bayes risk were obtained. The bounds improve the Brown-Gajek bound and the asymptotic expression is derived. As an application of the bound, lower bounds for the local minimax and Bayes prediction risk are also given. Furthermore, we generalized the skew-q-gaussian distribution by combining the skew distribution with the q-gaussian distribution. Recurrence formulae for the central moments were derived. The likelihood equation and Fisher information matrix were calculated. Moreover, the extreme value distribution was derived.
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