Yuliya Martsynyuk

Associate Professor

Office 336 Machray Hall
(204) 480-1074

Research Interests

Error-in-variables models, regression models, invariance principles and related limit theorems, self-normalized and Studentized partial sums processes, domains of attraction of normal laws, nonparametric change-point analysis, Gaussian processes

Recent Publications

  • Martsynyuk, Yu.V. (2016). Testing for change in the mean via convergence in distribution of sup-functionals of weighted tied-down partial sums processes. Mathematical Methods of Statistics 25, 219–232.
  • Martsynyuk, Yu.V. and Tuzov, E. (2016). Exploring functional CLT confidence intervals for a population mean in the domain of attraction of the normal law. Acta Mathematica Hungarica 148, 493–508.
  • Csorgo, M., Martsynyuk, Yu.V., Nasari, M.M. (2014). Another look at bootstrapping the Student t-statistic. Mathematical Methods of Statistics 23, 256-278.
  • Martsynyuk, Yu.V. (2013). On consistency of the least squares estimators in linear errors-in-variables models with infinite variance errors. The Electronic Journal of Statistics 7, 2851-2874.
  • Martsynyuk, Yu.V. (2013). On the generalized domain of attraction of the multivariate normal law and asymptotic normality of the multivariate Student t-statistic. Journal of Multivariate Analysis 114, 402-411.

→ See more publications

Comprehensive List of Research Interests

Error-in-variables models, regression models, reliability ratio and studies in error-in-variables models, infinite variance in regression and error-in-variables models, univariate and multivariate central limit theorems, functional central limit theorems, sup-norm approximations in probability in metric spaces, weak convergence in metric spaces, (vector) partial sums processes, self-normalization and Studentization, univariate and multivariate Student statistics, domain of attraction of the normal law, generalized domain of attraction of the multivariate normal law, asymptotic confidence intervals/regions, nonparametric change-point analysis, weak invariance principles in weighted sup-norms, Wiener and related Gaussian processes, weak and strong consistency, weakly dependent sequences of random variables.

Publications

Papers in Refereed Journals

  1. Martsynyuk, Yu.V. (2013). On consistency of the least squares estimators in linear errors-in-variables models with infinite variance errors. The Electronic Journal of Statistics. 7 2851-2874
  2. Martsynyuk, Yu.V. (2013). On the generalized domain of attraction of the multivariate normal law and asymptotic normality of the multivariate Student t-statistic. Journal of Multivariate Analysis. 114 402-411
  3. Martsynyuk, Yu.V. (2012). Invariance principles for a multivariate Student process in the generalized domain of attraction of the multivariate normal law. Statistics and Probability Letters. 82 2270-2277
  4. Csorgo, M. and Martsynyuk, Yu.V. (2011). Functional central limit theorems for self-normalized least squares processes in regression with possibly infinite variance data. Stochastic Processes and their Applications. 121 2925–2953
  5. Martsynyuk, Yu.V. (2009a). Functional asymptotic confidence intervals for the slope in linear error-in-variables models. Acta Mathematica Hungarica. 123 133-168
  6. Martsynyuk, Yu.V. (2009b). Functional asymptotic confidence intervals for a common mean of independent random variables. The Electronic Journal of Statistics. 3 25-40
  7. Martsynyuk, Yu.V. (2007a). New multivariate central limit theorems in linear structural and functional error-in-variables models. The Electronic Journal of Statistics. 1 347-380
  8. Martsynyuk, Yu.V. (2007b). Central limit theorems in linear structural error-in-variables models with explanatory variables in the domain of attraction of the normal law. The Electronic Journal of Statistics. 1 195-222
  9. Kukush, A.G. and Martsynyuk, Yu.V. (1999/2000). A criterion for the consistency of the least squares estimator for a functional linear model with errors in variables. Teoriya Imovirnostey ta Matematichna Statistika/Theory of Probability and Mathematical Statistics. 60 95-101/105-112 in Ukrainian/English
  10. Kukush, A.G. and Martsynyuk, Yu.V. (1998). Consistency and inconsistency of the weighted least squares estimator in linear functional error-in-variables models.*Theory of Stochastic Processes*. 4(20) 172-179

Technical Reports

  1. Martsynyuk, Yu.V. (2008). Functional asymptotic confidence intervals for the slope in linear error-in-variables models. Technical Report Series of the Laboratory for Research in Statistics and Probability. 441-March 2008 Carleton University-University of Ottawa.
  2. Martsynyuk, Yu.V. (2007). Invariance principles for self-randomized Student processes. Technical Report Series of the Laboratory for Research in Statistics and Probability. 438-March 2007 Carleton University-University of Ottawa.
  3. Martsynyuk, Yu.V. (2006). Studentized and self-normalized central limit theorems in linear functional error-in-variables models.*Technical Report Series of the Laboratory for Research in Statistics and Probability*. 432- May 2006 Carleton University-University of Ottawa.
  4. Martsynyuk, Yu.V. (2006). New central limit theorems via Studentization in linear structural error-in-variables models.*Technical Report Series of the Laboratory for Research in Statistics and Probability*. 428-February 2006 Carleton University-University of Ottawa.
  5. Martsynyuk, Yu.V. (2004). Invariance principles via Studentization in linear structural error-in-variables models. Technical Report Series of the Laboratory for Research in Statistics and Probability. 406-October 2004 Carleton University-University of Ottawa.
  6. Martsynyuk, Yu. V. (2001). Asymptotic behaviour of weighted least squares estimator in linear functional error-in-variables models.*Technical Report Series of the Laboratory for Research in Statistics and Probability*. 353-July 2001 Carleton University-University of Ottawa.

Thesis

  • Martsynyuk, Yu.V. (2005). Invariance Principles via Studentization in Linear Structural and Functional Error-in-Variables Models. Ph.D. Thesis. Carleton University, Ottawa.
Important Dates

December 11 – December 21: Fall Term Exam Period (includes tests and midterm exams for Fall/Winter Term classes)

December 22 – January 1: Winter Holiday (University Closed)

Upcoming Exams

STAT 3470 A01 Final Exam
Friday, December 15 at 9:00 a.m.

STAT 2000 Final Exam
Saturday, December 16 at 1:30 p.m.

STAT 1000 Final Exam
Saturday, December 16 at 6:00 p.m.

STAT 4100 A01 Final Exam
Saturday, December 16 at 6:00 p.m.

Upcoming Seminar

PIMS lecture: Pauline van den Driessche — Thursday, January 18 at 4 p.m., Robert Schultz Theatre.

Where are they now?

Yung-Ming Chang, Ph.D. (2002)

Yanqing Yi, Ph.D. (2008)