Estimation of a discrete probability under constraint of $k$-monotonicity
| Autor: | Jade Giguelay |
|---|---|
| Přispěvatelé: | Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de la Recherche Agronomique (INRA), Giguelay, Jade |
| Rok vydání: | 2017 |
| Předmět: |
Statistics and Probability
[SDV]Life Sciences [q-bio] 010102 general mathematics Estimator Mathematics - Statistics Theory Monotonic function non-parametric estimation shape constraint Statistics Theory (math.ST) Shape constraint $k$-monotone discrete probability 01 natural sciences Least squares 010104 statistics & probability Spline (mathematics) Support Reduction Algorithm FOS: Mathematics Applied mathematics 0101 mathematics Statistics Probability and Uncertainty Mathematics |
| Zdroj: | Electronic journal of statistics Electronic journal of statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2017, 11, pp.1-49. ⟨10.1214/16-EJS1220⟩ Electron. J. Statist. 11, no. 1 (2017), 1-49 Electronic Journal of Statistics (11), 1-49. (2017) Electronic Journal of Statistics Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2017, 11, pp.1-49. ⟨10.1214/16-EJS1220⟩ |
| ISSN: | 1935-7524 |
| DOI: | 10.1214/16-ejs1220 |
| Popis: | We propose two least-squares estimators of a discrete probability under the constraint of k-monotony and study their statistical properties. We give a characterization of these estimators based on the decomposition on a spline basis of k-monotone sequences. We develop an algorithm derived from the Support Reduction Algorithm and we finally present a simulation study to illustrate their properties. Comment: 53 pages, 35 figures |
| Databáze: | OpenAIRE |
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