| Presentation | 2003/7/21 Statistical Inference in Singular Models Kenji FUKUMIZU, Satoshi KURIKI, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Parametric statistical models are often used for inference and learning from data. A statiscal model is called "singlar" if the model, which is regarded as a subset of a functional space, has a non-smooth point. There are many singular models among popular statistical models such as mixture models, ARMA, and HMM. If a model has such singularity, we see many interesting behavior on the learning machine or statistical model. After reviewing the standard theory on the models without singularities, this paper discusses known theoretical results on statistical behavior of the parameter obtained by estimation or learning. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Statistical inference / singular model / likelihood ratio / tangent cone |
| Paper # | NC2003-27 |
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| Conference Information | |
| Committee | NC |
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| Conference Date | 2003/7/21(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
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| Paper Information | |
| Registration To | Neurocomputing (NC) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Statistical Inference in Singular Models |
| Sub Title (in English) | |
| Keyword(1) | Statistical inference |
| Keyword(2) | singular model |
| Keyword(3) | likelihood ratio |
| Keyword(4) | tangent cone |
| 1st Author's Name | Kenji FUKUMIZU |
| 1st Author's Affiliation | Institute of Statistical Mathematics() |
| 2nd Author's Name | Satoshi KURIKI |
| 2nd Author's Affiliation | Institute of Statistical Mathematics |
| Date | 2003/7/21 |
| Paper # | NC2003-27 |
| Volume (vol) | vol.103 |
| Number (no) | 227 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |