A geometric approach to statistical estimation
| Autor: | R. Kulhavy |
|---|---|
| Rok vydání: | 2002 |
| Předmět: |
Statistics::Theory
Kullback–Leibler divergence Statistical distance Shannon's source coding theorem Principle of maximum entropy Statistics::Computation Differential entropy Rényi entropy Statistics::Machine Learning Total variation distance of probability measures Statistics Maximum entropy probability distribution Statistics::Methodology Applied mathematics Mathematics |
| Zdroj: | Proceedings of 1995 34th IEEE Conference on Decision and Control. |
| DOI: | 10.1109/cdc.1995.480237 |
| Popis: | The role of Kerridge inaccuracy, Shannon entropy and Kullback-Leibler distance in statistical estimation is shown for both discrete and continuous observations. The cases of data independence and regression-type dependence are considered in parallel. Pythagorean-like relations valid for probability distributions are presented and their importance for estimation under compressed data is indicated. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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