Yuliana Yu. Linke

linke@math.nsc.ru
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  • Date of birth: May 5, 1975
  • Education:
    1992-1998, Novosibirsk State University, Mathematical Department
    1998-2000, Post Graduate Course, Chair of Probability Theory and Math. Statistics, NSU
    2000, Candidate of Science (Ph.D), Sobolev Institute of Mathematics
    2024, Doctor Habilitatus (Dr.Habil), Lomonosov Moscow State University
  • Positions:
    Senior Researcher, Laboratory of Applied Inverse Problems, Sobolev Institute of Mathematics;
    Associate Professor, Chair of Probability Theory and Mathematical Statistics, Novosibirsk State University.
  • Teaching materials for students of Department of Natural Sciences, NSU [in Russian]: pdf
  • Area of scientific research:
    Regression analysis, change-point problem.

Selected publications

  • Y.Y. Linke, I.S. Borisov. Universal nonparametric kernel-type estimators for the mean and covariance functions of a stochastic process. Teor. Veroyatnost. i Primenen. 69:1 (2024), 46-75 [in Russian]; Theory Probab. Appl. 69:1 (2024), P.35-58.
  • Y. Linke, I. Borisov, P. Ruzankin, V. Kutsenko, E. Yarovaya, S. Shalnova. Multivariate Universal Local Linear Kernel Estimators in Nonparametric Regression: Uniform Consistency. Mathematics. 12:12 (2024), 1890.
  • Yu.Yu. Linke. Towards insensitivity of Nadaraya--Watson estimators with respect to design correlation. Teor. Veroyatnost. i Primenen. 68:2 (2023), 236-252[in Russian]; Theory Probab. Appl. 68:2(2023), 198-210.
  • Yu.Yu. Linke. On sufficient conditions of the consistency of local linear kernel estimators. Mat. Zametki. 114:3 (2023), 353-369.
  • Yu.Yu. Linke, I.S. Borisov, P.S. Ruzankin Universal kernel-type estimation of random fields. Statistics. 57:4 (2023), 785-810.
  • Yu.Yu. Linke. Kernel estimators for the mean function of a stochastic process under sparse design conditions Siberian Adv. Math. 32:4 (2022), 269-276.
  • 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, I.S.Borisov. Insensitivity of Nadaraya--Watson estimators to design correlation. Communications in Statistics - Theory and Methods. 51:19 (2022), 6909-6918
  • 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. Asymptotic properties of one-step M-estimators. Communications in Statistics - Theory and Methods., 48:12 (2019), 4096-4118
  • Yu.Yu. Linke, I.S. Borisov. Constructing explicit estimators in nonlinear regression models. Teor. Veroyatnost. i Primenen., 63:1 (2018), 29-56[in Russian]; Theory Probab. Appl. 63:1(2018), 22-44
  • 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. Asymptotic properties of one-step weighted M-estimators with application to some regression problems. Teor. Veroyatnost. i Primenen., 62:3 (2017), 468-498[in Russian]; Theory Probab. Appl. 62:3(2018), 373-398
  • 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. Two-step estimition in some heteroscedastic linear regression model. Sib. J. Pure and Appl. Math., 17:2 (2017), 39-51[in Russian]; J. Math. Sci. 231:2 (2018), 206-217
  • Yu. Yu. Linke. Refinement of Fisher's one-step estimators in the case of slowly converging preliminary estimators. Teor. Veroyatnost. i Primenen., 60:1 (2015), 80-98[in Russian]; Theory Probab. Appl. 60:1(2016), 88-102
  • Yu. Yu. Linke, A. I. Sakhanenko. On conditions for one-step M-estimators to be asymptotically normal. Sib. J. Pure and Appl. Math., 16:4 (2016), 46-64.
  • Yu. Yu. Linke, A. I. Sakhanenko. On conditions for asymptotic normality of Fisher's one-step estimators in one-parameter families of distributions Sib. Elektron. Mat. Izv., 11 (2014), 464-475.
  • Yu. Yu. Linke, A. I. Sakhanenko. On asymptotics of the distributions of some two-step statistical estimators of a mutlidimensional parameter. Mat. Tr., 16:1 (2013), 89-120
  • Yu. Yu. Linke, A. I. Sakhanenko. On asymptotics of the distribution of a two-step statistical estimator of a one-dimensional parameter. Sib. Elektron. Mat. Izv., 10 (2013), 627-640.
  • Yu. Yu. Linke. On the asymptotics of distributions of two-step statistical estimates. Sibirsk. Mat. Zh., 52:4 (2011), 841-860.
  • Yu. Yu. Linke, A. I. Sakhanenko. On solutions to the equation for improving additives in regression problems. Mat. Tr., 14:2 (2011), 127-146.
  • A. I. Sakhanenko, Yu. Yu. Linke. Consistent estimation in a linear regression problem with random errors in coefficients. Sibirsk. Mat. Zh., 52:4 (2011), 894-912.
  • A. I. Sakhanenko, Yu. Yu. Linke. Improvement of estimators in a linear regression problem with random errors in coefficients. Sibirsk. Mat. Zh., 52:1 (2011), 143-160.
  • Yu. Yu. Linke, A. I. Sakhanenko. Asymptotically optimal estimation in a linear regression problem with random errors in coefficients. Sibirsk. Mat. Zh., 51:1 (2010), 128-145.
  • Yu. Yu. Linke, A. I. Sakhanenko. Asymptotically optimal estimation in the linear regression problem in the case of violation of some classical assumptions. Sibirsk. Mat. Zh., 50:2 (2009), 380-396.
  • Yu. Yu. Linke, A. I. Sakhanenko. Asymptotically normal estimation in the linear-fractional regression problem with random errors in coefficients. Sibirsk. Mat. Zh., 49:3 (2008), 592-619.
  • A. I. Sakhanenko, Yu. Yu. Linke. Asymptotically optimal estimation in a linear-fractional regression problem with random errors in coefficients. Sibirsk. Mat. Zh., 47:6 (2006), 1372-1400.
  • 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.
  • A.A. Borovkov, Yu.Yu. Linke. Asymptotically optimal estimates in the smooth change-point problem. Math. Methods of Statistics, 13:1 (2004), 1-24.
  • 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.
  • Yu. Yu. Linke, A. I. Sakhanenko. Asymptotically normal explicit estimation of parameters in the Michaelis–Menten equation. Sibirsk. Mat. Zh., 42:3 (2001), 610-633.
  • Yu. Yu. Linke, A. I. Sakhanenko. Asymptotically normal estimation of a multidimensional parameter in the linear-fractional regression problem. Sibirsk. Mat. Zh., 42:2 (2001), 372-388.
  • 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.
  • Yu. Yu. Linke, A. I. Sakhanenko. Asymptotically normal estimation of a parameter in a linear-fractional regression problem. Sibirsk. Mat. Zh., 41:1 (2000), 150-163.
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