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
Yu. Yu. Linke, I. S. Borisov, Teor. Veroyatnost. i Primenen.
2025
2.
I. S. Borisov, Y. Y. Linke, “Universal kernel-type estimators for the conditional variance in heteroscedastic models of nonparametric regression”, Siberian Advances in Mathematics, 35:4 (2025), 277-286
3.
Y. Y. Linke, I. S. Borisov, P. S. Ruzankin, V. A. Kutsenko, E. B. Yarovaya, S.A. Shalnova, “Multivariate Universal Local Linear Kernel Estimators in Nonparametric Regression: Simulations and Real Data Processing”, Sankhya A., 2025, 1-28
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
5.
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.
Y. Y. Linke, “On the accuracy of the uniform approximation of universal local constant kernel estimators to smooth regression functions”, Sib. Èlektron. Mat. Izv., 21:2 (2024), 1450–1459
2023
7.
Yu. Yu. Linke, “Towards insensitivity of Nadaraya–Watson estimators with respect to design correlation”, Theory Probab. Appl., 68:2 (2023), 198–210
8.
Yu. Yu. Linke, “On Sufficient Conditions for the Consistency of Local Linear Kernel Estimators”, Math. Notes, 114:3 (2023), 308–321
9.
Y.Y. Linke, I.S. Borisov, P.S. Ruzankin, “Universal kernel-type estimation of random fields”, Statistics, 57:4 (2023), 785-810
Yu. Yu. Linke, “Mean function estimation for a noisy random process under a sparse data condition”, Chebyshevskii Sb., 24:5 (2023), 112–125
11.
Yu. Yu. Linke, I. S. Borisov, “An approach to constructing explicit estimators in nonlinear regression”, Siberian Adv. Math., 33:4 (2023), 338–346
2022
12.
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
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
Yu. Yu. Linke, “Kernel estimators for the mean function of a stochastic process under sparse design conditions”, Mat. Tr., 25:2 (2022), 149–161
2021
15.
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
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
Yu. Yu. Linke, I. S. Borisov, “Constructing explicit estimators in nonlinear regression problems”, Theory Probab. Appl., 63:1 (2018), 22–44
20.
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
21.
Yu. Yu. Linke, “Two-step estimation in heteroscedastic linear regression model”, J. Math. Sci., 231:2 (2018), 206–217
2017
22.
Yu. Yu. Linke, I. S. Borisov, “Constructing initial estimators in one-step estimation procedures of nonlinear regression”, Statist. Probab. Lett., 120 (2017), 87-94
Yu. Yu. Linke, A. I. Sakhanenko, “Conditions of asymptotic normality of one-step $M$-estimators”, J. Math. Sci., 230:1 (2018), 95–111
2016
25.
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
2014
26.
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
27.
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
2013
28.
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
2012
29.
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
2011
30.
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
31.
Yu. Yu. Linke, “On the asymptotics of distributions of two-step statistical estimates”, Siberian Math. J., 52:4 (2011), 665–681
32.
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
2010
33.
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
2009
34.
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
2008
35.
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
2006
36.
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
2005
37.
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
2004
38.
A. A. Borovkov, Yu. Yu. Linke, “Asymptotically optimal estimates in the smooth change-point problem”, Math. Methods of Statistics, 13:1 (2004), 1-24
2003
39.
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
2001
40.
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
41.
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
2000
42.
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
43.
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
2025
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