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Zhigljavsky A., Golyandina N., Gryaznov S. Deconvolution of a discrete uniformdistribution // Stat Probabil Lett. 2016. Vol. 118. P. 37–44.24. Kukush A., Markovsky I., Van Huffel S. Consistency of the structured totalleast squares estimator in a multivariate errors-in-variables model // Journal ofStatistical Planning and Inference. 2005. Vol. 133, no. 2.
P. 315–358.25. The element-wise weighted total least-squares problem / Ivan Markovsky,Maria Luisa Rastello, Amedeo Premoli et al. // Computational Statistics &Data Analysis. 2006. Vol. 50, no. 1. P. 181–209.26. De Moor B. Total least squares for affinely structured matrices and the noisyrealization problem // IEEE Transactions on Signal Processing. 1994. Vol. 42,no. 11. P. 3104–3113.27.
Звонарев Н. К. Поиск весов в задаче взвешенной аппроксимации временнымрядом конечного ранга // Вестник Санкт-Петербургского университета. Серия 1. Математика. Механика. Астрономия. 2016. Т. 3, № 4.28. Zvonarev N., Golyandina N. Iterative algorithms for weighted and unweightedfinite-rank time-series approximations // Statistics and Its Interface. 2017.Vol. 10, no. 1. P. 5–18.29. Звонарев Н., Голяндина Н. Итеративные алгоритмы взвешенной аппроксимации рядами конечного ранга // System Identification And ControlProblems.
SICPRO’15. 2015. P. 1371–1394.30. Zvonarev N., Golyandina N. Modified Gauss-Newthon method in low-rank signalestimation. arXiv:1803.01419.31. Hall M. Combinatorial Theory. Wiley-Interscience, 1998.14932. Usevich K. On signal and extraneous roots in singular spectrum analysis //Statistics and Its Interface.
2010. Vol. 3, no. 3. P. 281–295.33. Heinig G., Rost. Algebraic Methods for Toeplitz-like Matrices and Operators(Operator Theory: Advances and Applications). Birkhäuser Verlag, 1985.34. Stewart G. On scaled projections and pseudoinverses // Linear Algebra and itsApplications. 1989. jan. Vol. 112. P. 189–193.35. Nocedal J., Wright S. Numerical optimization. Springer Science & BusinessMedia, 2006.36.
Golub G., Pereyra V. Separable nonlinear least squares: the variable projectionmethod and its applications // Inverse problems. 2003. Vol. 19, no. 2. P. R1.37. Gillard J., Zhigljavsky A. Weighted norms in subspace-based methods for timeseries analysis // Numerical Linear Algebra with Applications. 2016. Vol. 23,no. 5. P.
947–967.38. Lewis A. S., Malick J. Alternating projections on manifolds // Mathematics ofOperations Research. 2008. Vol. 33, no. 1. P. 216–234.39. Bounds for the rank of the sum of two matrices : Rep. / Boeing ScientificResearch Labs Seattle WA ; Executor: George Marsaglia : 1964.40. Power Sums, Gorenstein Algebras, and Determinantal Loci / A. Iarrobino,A.
Iarrobino, V. Kanev, S.L. Kleiman. Lecture Notes in Mathematics. SpringerBerlin Heidelberg, 1999.41. Shiryaev A. N. Convergence of probability measures. central limit theorem //Probability-1. Springer, 2016. P. 373–460.42. Kay S. M.
Fundamentals of Statistical Signal Processing, Volume I: EstimationTheory (v. 1). Prentice Hall, 1993.43. Davis P. J. Circulant matrices. American Mathematical Soc., 2012.44. Graillat S., Ménissier-Morain V. Compensated Horner scheme in complexfloating point arithmetic // Proceedings of the 8th Conference on Real Numbersand Computers, Santiago de Compostela, Spain. 2008.
P. 133–146.15045. Brent R. P. Algorithms for Minimisation without Derivatives (AutomaticComputation). Prentice Hall, 1972.46. Kiefer J. Sequential minimax search for a maximum // Proceedings of theAmerican Mathematical Society. 1953. mar. Vol. 4, no. 3. P. 502–502.47. Gillard J., Zhigljavsky A. Optimization challenges in the structured low rankapproximation problem // Journal of Global Optimization. 2013. Vol.
57, no. 3.P. 733–751.48. Chu M. T., Funderlic R. E., Plemmons R. J. Structured low rank approximation // Linear Algebra and its Applications. 2003. Vol. 366, no. 0. P. 157 – 172.Special issue on Structured Matrices: Analysis, Algorithms and Applications.49. Verbyla A. A note on the inverse covariance matrix of the autoregressiveprocess // Australian & New Zealand Journal of Statistics.
1985. Vol. 27,no. 2. P. 221–224.50. Kullback S., Leibler R. A. On information and sufficiency // The annals ofmathematical statistics. 1951. Vol. 22, no. 1. P. 79–86.51. Duchi J. Derivations for linear algebra and optimization // Berkeley, California.2007.52. Amari S.-i., Nagaoka H.
Methods of information geometry, volume 191 oftranslations of mathematical monographs // American Mathematical Society.2000. Vol. 13.53. Theodoridis S. Machine learning: a Bayesian and optimization perspective.Academic Press, 2015.54. Гавурин М., Малоземов В. Экстремальные задачи с линейными ограничениями. Учебное пособие. 1984.55. A limited memory algorithm for bound constrained optimization /Richard H. Byrd, Peihuang Lu, Jorge Nocedal, Ciyou Zhu // SIAM Journalon Scientific Computing.
1995. sep. Vol. 16, no. 5. P. 1190–1208.56. Korobeynikov A. Computation- and space-efficient implementation of SSA //151Statistics and Its Interface. 2010. Vol. 3, no. 3. P. 357–368.57. Von Neumann J. Functional Operators: The Geometry of Orthogonal Spaces.Annals of Mathematics Studies. Princeton University Press, 1950.58. Meyer C. D.
Matrix analysis and applied linear algebra. SIAM: Society forIndustrial and Applied Mathematics, 2001.59. Optimal rates of convergence of matrices with applications / Heinz H. Bauschke,J. Y. Bello Cruz, Tran T. A. Nghia et al. arXiv : math.OC/1407.0671v1.60. Belousov S. L. Tables of Normalized Associated Legendre Polynomials:Mathematical Tables Series. Pergamon, 2014.61. Golyandina N. On the choice of parameters in singular spectrum analysisand related subspace-based methods // Stat. Interface.
2010. Vol. 3, no. 3.P. 259–279.62. Two-exponential models of gene expression patterns for noisy experimentaldata / Theodore Alexandrov, Nina Golyandina, David Holloway et al.arXiv:1704.00351v1.63. Gardner G., Harvey A. C., Phillips G. D. A. Algorithm AS 154: An algorithm forexact maximum likelihood estimation of autoregressive-moving average modelsby means of kalman filtering // Applied Statistics. 1980. Vol.
29, no. 3. P. 311.64. Andrews D., Herzberg A. Data: a collection of problems from many fields forthe student and research worker. Springer series in statistics. Springer-Verlag,1985.65. Golyandina N., Shlemov A. Variations of singular spectrum analysis forseparability improvement: non-orthogonal decompositions of time series.arXiv:1308.4022v2.66. Schwarz G. Estimating the dimension of a model // The Annals of Statistics.1978. mar.
Vol. 6, no. 2. P. 461–464.67. Velasco C., Lobato I. N. et al. A simple and general test for white noise //Econometric Society 2004 Latin American Meetings. No. 112. 2004..