| Budget Amount *help |
¥5,070,000 (Direct Cost: ¥3,900,000、Indirect Cost: ¥1,170,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2013: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Outline of Final Research Achievements |
This project focuses on inverse problems to estimate the unknown parameters in errors-in-variables (EIV), multiplicative noise and discrete tomography models by combining different types of data. We achieve a number of new results and publish 9 papers in international journals. The applicant is invited to report the achieved results at several international conferences. We develop new LS-based estimators to reconstruct multiplicative error models and conduct quality evaluation of lattice basis reduction for discrete tomography, which can be directly affected by the probability distribution of the reduced Gram-Schmidt coefficients. We propose bias-corrected LS and N-calibrated estimators to estimate the EIV parameters, which are as good as total LS, but require much less computation. We prove that the variance components in an EIV model can be inestimable and unstable. We investigate how EIV can affect the parameter estimation and the weighting factors for different types of data.
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