| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2016: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2014: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Outline of Final Research Achievements |
In this study, we focus on the asymmetric kernel methods to estimate the probability density function for nonnegative data. Especially, we have proposed (i) a family of asymmetric kernel density estimators, depending on the so-called density generator that plays a role of an infinite dimensional parameter, and (ii) a family of Amoroso kernel density estimators, including the gamma/inverse gamma/inverse Gaussian/reciprocal inverse Gaussian/Birnbaum-Saunders/log-normal kernel density estimators as special cases of these families. Also, we have discussed (iii) some bias-reduced density estimators. We have established their asymptotic properties (bias, variance, mean integrated squared error, and so on), as well as the (pointwise) strong consistency and asymptotic normality. The simulation studies have been conducted to confirm our theoretical results.
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