Geodesic
Asymptotic efficiency of maximum entropy estimates
Doklady Rossijskoj akademii nauk. Matematika, informatika, processy upravleniâ, Tome 493 (2020), pp. 104-107 Cet article a éte moissonné depuis la source Math-Net.Ru

Voir la notice de l'article

The problem of entropy estimation of probability density functions with allowance for real data is posed (the maximum entropy estimation (MEE) problem). Global existence conditions for the implicit dependence of Lagrange multipliers on data collection are obtained. The asymptotic efficiency of maximum entropy estimates is proved.
Keywords: entropy estimation, density functions, vector field rotation, asymptotic efficiency.
Mots-clés : Lagrange multipliers 
@article{DANMA_2020_493_a20,
     author = {Yu. S. Popkov},
     title = {Asymptotic efficiency of maximum entropy estimates},
     journal = {Doklady Rossijskoj akademii nauk. Matematika, informatika, processy upravleni\^a},
     pages = {104--107},
     year = {2020},
     volume = {493},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/DANMA_2020_493_a20/}
}
TY  - JOUR
AU  - Yu. S. Popkov
TI  - Asymptotic efficiency of maximum entropy estimates
JO  - Doklady Rossijskoj akademii nauk. Matematika, informatika, processy upravleniâ
PY  - 2020
SP  - 104
EP  - 107
VL  - 493
UR  - http://geodesic.mathdoc.fr/item/DANMA_2020_493_a20/
LA  - ru
ID  - DANMA_2020_493_a20
ER  - 
%0 Journal Article
%A Yu. S. Popkov
%T Asymptotic efficiency of maximum entropy estimates
%J Doklady Rossijskoj akademii nauk. Matematika, informatika, processy upravleniâ
%D 2020
%P 104-107
%V 493
%U http://geodesic.mathdoc.fr/item/DANMA_2020_493_a20/
%G ru
%F DANMA_2020_493_a20
Yu. S. Popkov. Asymptotic efficiency of maximum entropy estimates. Doklady Rossijskoj akademii nauk. Matematika, informatika, processy upravleniâ, Tome 493 (2020), pp. 104-107. http://geodesic.mathdoc.fr/item/DANMA_2020_493_a20/

[1] Aivazyan S.A., Enyukov I.S., Meshalkin L.D., Prikladnaya statistika: Issledovanie zavisimostei, Finansy i statistika, M., 1985 | MR

[2] Goldberger A.S., A Course in Econometrics, Harvard University Press, 1991, 437 pp. | MR

[3] Hastie T., Tibshirani R., Friedman J., The Elements of Statistical Learning, Springer, 2001 | MR | Zbl

[4] Vorontsov K.V., Matematicheskie metody obucheniya po pretsendentam, Kurs lektsii MFTI, 2006

[5] Borovkov A.A., Matematicheskaya statistika, Nauka, M., 1984 | MR

[6] Lagutin M.B., Naglyadnaya matematicheskaya statistika, BINOM, Laboratoriya znanii, M., 2013

[7] Rousses G., A Course of the Mathematical Statistics, Academic Press Inc, 2015, 600 pp.

[8] Popkov Yu.S., Dubnov Yu.A., Popkov A.Yu., “Randomized Machine Learning: Statement, Solution, Applications”, IEEE 8th International Conference on Intelligent Systems (IS), 2016 | DOI | MR

[9] Popkov A.Yu., Popkov Yu.S., “New Methods of Entropy-Robust Estimation for Randomized Models under Limited Data”, Entropy, 2014, no. 16, 675–698 | DOI

[10] Ioffe A.D., Tikhomirov V.M., Teoriya ekstremalnykh zadach, Nauka, M., 1974, 481 pp. | MR