Geodesic
Semiparametric reconstruction of the density function based on a~generalized lambda-distribution in the problem of identification of regression models
Sibirskij žurnal industrialʹnoj matematiki, Tome 17 (2014) no. 3, pp. 71-77.

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The problem of estimating the parameters of regression models is considered. We study the method of the adaptive estimation of the parameters of regression models with the use of the semiparametric approach to the estimation of the density distribution function of random errors. The accuracy of the estimation of the parameters of the regression dependencies of this method is compared with the results obtained by the adaptive method on the basis of the generalized lambda-distribution developed earlier by the authors.
Keywords: regression dependency, adaptive estimation, nuclear estimate, generalized lambda-distribution, maximum likelihood method
Mots-clés : identification of a distribution. 
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     author = {V. I. Denisov and V. S. Timofeev and E. A. Khailenko},
     title = {Semiparametric reconstruction of the density function based on a~generalized lambda-distribution in the problem of identification of regression models},
     journal = {Sibirskij \v{z}urnal industrialʹnoj matematiki},
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V. I. Denisov; V. S. Timofeev; E. A. Khailenko. Semiparametric reconstruction of the density function based on a~generalized lambda-distribution in the problem of identification of regression models. Sibirskij žurnal industrialʹnoj matematiki, Tome 17 (2014) no. 3, pp. 71-77. http://geodesic.mathdoc.fr/item/SJIM_2014_17_3_a6/

[1] Borovkov A. A., Matematicheskaya statistika. Otsenka parametrov, proverka gipotez, Nauka, M., 1984 | MR | Zbl

[2] Timofeev V. S., “Adaptivnoe vosstanovlenie regressionnykh zavisimostei na osnove poluparametricheskoi otsenki plotnosti sluchainoi komponenty”, Nauch. vestn. NGTU, 2013, no. 4, 24–30

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

[4] Olkin I., Spiegelman C. H., “A semiparametric approach to density estimation”, J. Amer. Statist. Assoc., 82:399 (1987), 858–865 | DOI | MR | Zbl

[5] Karian Z. A., Dudewicz E. J., Fitting Statistical Distributions: the Generalized Lambda Distribution and Generalized Bootstrap methods, CRC Press, N.Y., 2000 | MR | Zbl

[6] Lakhany A., Mausser H., “Estimation the parameters of the generalized lambda distribution”, ALGO Research Quarterly, 3:3 (2000), 27–58

[7] Pagan A., Ullah A., Nonparametric Econometrics, Univ. Press, Cambridge, 1999 | MR

[8] Bandi B., Metody optimizatsii. Vvodnyi kurs, Radio i svyaz, M., 1988

[9] Timofeev V. S., Khailenko E. A., “Adaptivnoe otsenivanie parametrov regressionnykh modelei s ispolzovaniem obobschennogo lyambda-raspredeleniya”, Dokl. AN vyssh. shkoly (Novosibirsk), 2010, no. 2(15), 25–36