Persons: Linke, Yuliana Yurievna

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Linke, Yuliana Yurievna

Total publications:	39 (39)
in MathSciNet:	17 (17)
in zbMATH:	8 (8)
in Web of Science:	13 (13)
in Scopus:	13 (13)
Cited articles:	31
Citations:	220
Presentations:	5

Number of views:
This page:	1776
Abstract pages:	7738
Full texts:	2100
References:	1052

Doctor of physico-mathematical sciences (2024)

Speciality:

01.01.05 (Probability theory and mathematical statistics)

Birth date:

5.05.1975

E-mail:

Subject:

nonlinear and nonparametric regression, kernel estimators, one-step estimators, change-point problem

Main publications:

Yu.Yu. Linke, Asymptotic properties of one-step M-estimators., Communications in Statistics - Theory and Methods., 48, (2019), 4096-4118
Linke Y., Borisov I., Ruzankin P., Kutsenko V., Yarovaya E., Shalnova S., “Universal local linear kernel estimators in nonparametric regression”, Mathematics, 10:15 (2022), 2693.
Yu.Yu. Linke, I.S. Borisov, Constructing initial estimators in one-step estimation procedures of nonlinear regression, Stat. Probab. Lett., 120, (2017), 87-94
Yu.Yu. Linke, Asymptotic normality of one-step M-estimators based on non-identically distributed observations., Stat. Probab. Lett., 129, (2017), 216-221
Yu.Yu. Linke, I.S,Borisov, “Insensitivity of Nadaraya–Watson estimators to design correlation”, Communications in Statistics - Theory and Methods, 51:19 (2022), 6909-6918

https://www.mathnet.ru/eng/person17819

List of publications on Google Scholar

List of publications on ZentralBlatt

https://mathscinet.ams.org/mathscinet/MRAuthorID/662637

Full list of publications:

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common list

		Citations (Crossref Cited-By Service + Math-Net.Ru)

Articles

1.	Yu. Yu. Linke, I. S. Borisov, “Universal nonparametric kernel-type estimators for the mean and covariance functions of a stochastic process”, Theory Probab. Appl., 69:1 (2024), 35–58
2.	Linke Y. , Borisov I. , Ruzankin P. , Kutsenko V., Yarovaya E., Shalnova S., “Multivariate Universal Local Linear Kernel Estimators in Nonparametric Regression: Uniform Consistency”, Mathematics, 12:12 (2024), 1890 , 23 pp.
3.	Yu. Yu. Linke, “Towards insensitivity of Nadaraya–Watson estimators with respect to design correlation”, Theory Probab. Appl., 68:2 (2023), 198–210
4.	Yu. Yu. Linke, “On Sufficient Conditions for the Consistency of Local Linear Kernel Estimators”, Math. Notes, 114:3 (2023), 308–321
5.	Y.Y. Linke, I.S. Borisov, P.S. Ruzankin, “Universal kernel-type estimation of random fields”, Statistics, 57:4 (2023), 785-810	4 ^[x]
6.	Yu. Yu. Linke, “Mean function estimation for a noisy random process under a sparse data condition”, Chebyshevskii Sb., 24:5 (2023), 112–125
7.	Yu. Yu. Linke, I. S. Borisov, “An approach to constructing explicit estimators in nonlinear regression”, Siberian Adv. Math., 33:4 (2023), 338–346
8.	Yu. Yu. Linke, I. S. Borisov, “Insensitivity of Nadaraya–Watson estimators to design correlation”, Communications in Statistics – Theory and Methods, 51:19 (2022), 6909–6918	10 ^[x]
9.	Y. Linke, I. Borisov, P. Ruzankin, V. Kutsenko, E. Yarovaya, S. Shalnova., “Universal local linear kernel estimators in nonparametric regression”, Mathematics, 10:15 (2022), 2693	13 ^[x]
10.	Yu. Yu. Linke, “Kernel estimators for the mean function of a stochastic process under sparse design conditions”, Mat. Tr., 25:2 (2022), 149–161
11.	I. S. Borisov, Yu. Yu. Linke, P. S. Ruzankin, “Universal weighted kernel-type estimators for some class of regression models”, Metrika, 84:2 (2021), 141-166	12 ^[x]
12.	Yu. Yu. Linke, I. S. Borisov, “Toward the notion of intrinsically linear models in nonlinear regression”, Siberian Adv. Math., 29:3 (2019), 210-216	1 ^[x]
13.	Yu. Yu. Linke, “Asymptotic properties of one-step M-estimators”, Communications in Statistics – Theory and Methods, 48:16 (2019), 4096-4118	13 ^[x]
14.	Yu.Yu. Linke, “Kernel estimators for the mean function of a stochastic process under sparse design conditions”, Siberian Advances in Mathematics, 32:4 (2019), 269–276	1 ^[x]
15.	Yu. Yu. Linke, I. S. Borisov, “Constructing explicit estimators in nonlinear regression problems”, Theory Probab. Appl., 63:1 (2018), 22–44
16.	Yu. Yu. Linke, “Asymptotic properties of one-step weighted $M$-estimators with application to some regression problems.”, Theory Probab. Appl., 62:3 (2018), 373–398
17.	Yu. Yu. Linke, “Two-step estimation in heteroscedastic linear regression model”, J. Math. Sci., 231:2 (2018), 206–217
18.	Yu. Yu. Linke, I. S. Borisov, “Constructing initial estimators in one-step estimation procedures of nonlinear regression”, Statist. Probab. Lett., 120 (2017), 87-94	20 ^[x]
19.	Yu. Yu. Linke, “Asymptotic normality of one-step M-estimators based on non-identically distributed observations”, Statist. Probab. Lett., 129 (2017), 216-221	10 ^[x]
20.	Yu. Yu. Linke, A. I. Sakhanenko, “Conditions of asymptotic normality of one-step $M$-estimators”, J. Math. Sci., 230:1 (2018), 95–111
21.	Yu. Yu. Linke, “Refinement of Fisher’s one-step estimators in the case of slowly converging preliminary estimators”, Theory Probab. Appl., 60:1 (2016), 88–102
22.	Yu. Yu. Linke, A. I. Sakhanenko, “On conditions for asymptotic normality of Fisher's one-step estimators in one-parameter families of distributions”, Sib. Èlektron. Mat. Izv., 11 (2014), 464–475
23.	Yu. Yu. Linke, A. I. Sakhanenko, “On asymptotics of the distributions of some two-step statistical estimators of a mutlidimensional parameter”, Siberian Adv. Math., 24:2 (2014), 119–139
24.	Yu. Yu. Linke, A. I. Sakhanenko, “On asymptotics of the distribution of a two-step statistical estimator of a one-dimensional parameter”, Sib. Èlektron. Mat. Izv., 10 (2013), 627–640
25.	Yu. Yu. Linke, A. I. Sakhanenko, “On solutions to the equation for improving additives in regression problems”, Siberian Adv. Math., 22:4 (2012), 261–274
26.	A. I. Sakhanenko, Yu. Yu. Linke, “Consistent estimation in a linear regression problem with random errors in coefficients”, Siberian Math. J., 52:4 (2011), 711–726
27.	Yu. Yu. Linke, “On the asymptotics of distributions of two-step statistical estimates”, Siberian Math. J., 52:4 (2011), 665–681
28.	A. I. Sakhanenko, Yu. Yu. Linke, “Improvement of estimators in a linear regression problem with random errors in coefficients”, Siberian Math. J., 52:1 (2011), 113–126
29.	Yu. Yu. Linke, A. I. Sakhanenko, “Asymptotically optimal estimation in a linear regression problem with random errors in coefficients”, Siberian Math. J., 51:1 (2010), 104–118
30.	Yu. Yu. Linke, A. I. Sakhanenko, “Asymptotically optimal estimation in the linear regression problem in the case of violation of some classical assumptions”, Siberian Math. J., 50:2 (2009), 302–315
31.	Yu. Yu. Linke, A. I. Sakhanenko, “Asymptotically normal estimation in the linear-fractional regression problem with random errors in coefficients”, Siberian Math. J., 49:3 (2008), 474–497
32.	A. I. Sakhanenko, Yu. Yu. Linke, “Asymptotically optimal estimation in a linear-fractional regression problem with random errors in coefficients”, Siberian Math. J., 47:6 (2006), 1128–1153
33.	A. A. Borovkov, Yu. Yu. Linke, “Change-point problem for large samples and incomplete information on distribution”, Math. Methods of Statistics, 14:4 (2005), 404-430
34.	A. A. Borovkov, Yu. Yu. Linke, “Asymptotically optimal estimates in the smooth change-point problem”, Math. Methods of Statistics, 13:1 (2004), 1-24
35.	I. V. Askarova, Yu. Yu. Linke, “On conditions for the asymptotic normality of estimates of the second step in a linear-fractional regression problem”, Sib. Zh. Ind. Mat., 6:3 (2003), 8–17
36.	Yu. Yu. Linke, A. I. Sakhanenko, “Asymptotically normal explicit estimation of parameters in the Michaelis–Menten equation”, Siberian Math. J., 42:3 (2001), 517–536
37.	Yu. Yu. Linke, A. I. Sakhanenko, “Asymptotically normal estimation of a multidimensional parameter in the linear-fractional regression problem”, Siberian Math. J., 42:2 (2001), 317–331
38.	Yu. Yu. Linke, “Explicit asymptotically normal estimation of the parameter for a multidimensional nonlinear regression problem”, Sib. Zh. Ind. Mat., 3:1 (2000), 157–164
39.	Yu. Yu. Linke, A. I. Sakhanenko, “Asymptotically normal estimation of a parameter in a linear-fractional regression problem”, Siberian Math. J., 41:1 (2000), 125–137

Presentations in Math-Net.Ru

1.	Универсальные ядерные оценки в непараметрической регрессии с приложениями к нелинейным регрессионным моделям Yu. Yu. Linke Principle Seminar of the Department of Probability Theory, Moscow State University December 13, 2023 16:45
2.	Универсальные ядерные оценки в непараметрической регрессии Yu. Yu. Linke November 11, 2022 15:30
3.	Универсальные локально-постоянные и локально-линейные ядерные оценки в непараметрической регрессии Yu. Yu. Linke Principle Seminar of the Department of Probability Theory, Moscow State University March 2, 2022 16:45
4.	Построение явных оценок в задачах нелинейной регрессии с приложениями к непараметрическим регрессионным моделям Yu. Yu. Linke Principle Seminar of the Department of Probability Theory, Moscow State University April 21, 2021 16:45
5.	Асимптотические свойства одношаговых $M$-оценок с приложениями к задачам регрессии Yu. Yu. Linke Seminar on Probability Theory and Mathematical Statistics April 29, 2016 18:00

Organisations

Lomonosov Moscow State University, Faculty of Mechanics and Mathematics
Novosibirsk State University, Mechanics and Mathematics Department
Sobolev Institute of Mathematics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk

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