On a new estimation method of the Bernoulli regression function
Teoriâ veroâtnostej i ee primeneniâ, Tome 67 (2022) no. 2, pp. 209-222 Cet article a éte moissonné depuis la source Math-Net.Ru

Voir la notice de l'article

A new estimator for a Bernoulli regression function based on Bernstein polynomials is constructed. Its consistency and asymptotic normality are studied. A criterion for testing the hypothesis on the form of a Bernoulli regression function and a criterion for testing the hypothesis on the equality of two Bernoulli regression functions are constructed. The consistency of these two criteria is studied.
Keywords: Bernstein polynomial, Bernoulli regression function, consistency, power of a criterion, one-sided alternatives.
@article{TVP_2022_67_2_a0,
     author = {P. Babilua and \`E. A. Nadaraya},
     title = {On a~new estimation method of the {Bernoulli} regression function},
     journal = {Teori\^a vero\^atnostej i ee primeneni\^a},
     pages = {209--222},
     year = {2022},
     volume = {67},
     number = {2},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/TVP_2022_67_2_a0/}
}
TY  - JOUR
AU  - P. Babilua
AU  - È. A. Nadaraya
TI  - On a new estimation method of the Bernoulli regression function
JO  - Teoriâ veroâtnostej i ee primeneniâ
PY  - 2022
SP  - 209
EP  - 222
VL  - 67
IS  - 2
UR  - http://geodesic.mathdoc.fr/item/TVP_2022_67_2_a0/
LA  - ru
ID  - TVP_2022_67_2_a0
ER  - 
%0 Journal Article
%A P. Babilua
%A È. A. Nadaraya
%T On a new estimation method of the Bernoulli regression function
%J Teoriâ veroâtnostej i ee primeneniâ
%D 2022
%P 209-222
%V 67
%N 2
%U http://geodesic.mathdoc.fr/item/TVP_2022_67_2_a0/
%G ru
%F TVP_2022_67_2_a0
P. Babilua; È. A. Nadaraya. On a new estimation method of the Bernoulli regression function. Teoriâ veroâtnostej i ee primeneniâ, Tome 67 (2022) no. 2, pp. 209-222. http://geodesic.mathdoc.fr/item/TVP_2022_67_2_a0/

[1] S. Efromovich, Nonparametric curve estimation: methods, theory, and applications, Springer Ser. Statist., Springer-Verlag, New York, 1999, xiv+411 pp. | DOI | MR | Zbl

[2] J. B. Copas, “Plotting $p$ against $x$”, J. Roy. Statist. Soc. Ser. C, 32:1 (1983), 25–31 | DOI

[3] H. Okumura, K. Naito, “Weighted kernel estimators in nonparametric binomial regression”, The international conference on recent trends and directions in nonparametric statistics, J. Nonparametr. Stat., 16, no. 1-2, 2004, 39–62 | DOI | MR | Zbl

[4] M. Aerts, N. Veraverbeke, “Bootstrapping a nonparametric polytomous regression model”, Math. Methods Statist., 4:2 (1995), 189–200 | MR | Zbl

[5] G. G. Lorentz, Bernstein polynomials, 2nd ed., Chelsea Publishing Co., New York, 1986, x+134 pp. | MR | Zbl

[6] E. Nadaraya, P. Babilua, G. Sokhadze, “About the nonparametric estimation of the Bernoulli regression”, Comm. Statist. Theory Methods, 42:22 (2013), 3989–4002 | DOI | MR | Zbl

[7] A. Leblanc, “On estimating distribution functions using Bernstein polynomials”, Ann. Inst. Statist. Math., 64:5 (2012), 919–943 | DOI | MR | Zbl

[8] H. Cramér, Mathematical methods of statistics, Princeton Math. Ser., 9, Princeton Univ. Press, Princeton, NJ, 1946, xvi+575 pp. | MR | MR | Zbl

[9] A. N. Shiryayev, Probability, Grad. Texts in Math., 95, Springer-Verlag, New York, 1984, xi+577 pp. | DOI | MR | MR | Zbl | Zbl

[10] W. Feller, An introduction to probability theory and its applications, v. 2, John Wiley Sons, Inc., New York–London–Sydney, 1966, xviii+626 pp. | MR | MR | Zbl | Zbl

[11] P. K. Bhattacharya, J. L. Gastwirth, “Estimation of the odds-ratio in an observational study using bandwidth-matching”, First NIU symposium on statistical sciences (De Kalb, IL, 1996), J. Nonparametr. Statist., 11, no. 1-3, 1999, 1–12 | DOI | MR | Zbl

[12] P. K. Babilua, É. A. Nadaraya, G. A. Sokhadze, “On the square-integrable measure of the divergence of two nuclear estimations of the Bernoulli regression functions”, Ukrainian Math. J., 67:1 (2015), 1–18 | DOI | MR | Zbl

[13] E. Abbe, “Über die Gesetzmässigkeit in der Vorteilung der Fehler bei Beobachtungsreihen”, Gesammelte Abhandlungen, v. 2, Jena, G. Fischer, 1906, 55–81 | Zbl

[14] J. W. Linnik, Method of least squares and principles of the theory of observations, Pergamon Press, New York–Oxford–London–Paris, 1961, xii+360 pp. | MR | MR | Zbl | Zbl

[15] R. Absava, E. Nadaraya, “Limit distribution of the mean square deviation of the Gasser–Müller nonparametric estimate of the regression function”, Georgian Math. J., 6:6 (1999), 501–516 | MR | Zbl