Divergence Measures Estimation and Its Asymptotic Normality Theory Using Wavelets Empirical Processes I
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Bibliographic record
Abstract
We deal with the normality asymptotic theory of empirical divergences measures based on wavelets in a series of three papers.In this first paper, we provide the asymptotic theory of the general of φ -divergences measures, which includes the most common divergence measures : Renyi and Tsallis families and the Kullback-Leibler measures.Instead of using the Parzen nonparametric estimators of the probability density functions whose discrepancy is estimated, we use the wavelets approach and the geometry of Besov spaces.One-sided and two-sided statistical tests are derived.This paper is devoted to the foundations the general asymptotic theory and the exposition of the mains theoretical tools concerning the φ -forms, while proofs and next detailed and applied results will be given in the two subsequent papers which deal important key divergence measures and symmetrized estimators.
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Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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