[1]	A. Fazzi, A. Kukush, and I. Markovsky. Bias correction for Vandermonde low-rank approximation. Econometrics and Statistics, 31:38--48, 2024. [ bib \| DOI \| Abstract ]
[2]	I. Markovsky and H. Ossareh. Finite-data nonparametric frequency response evaluation without leakage. Automatica, 159:111351, 2024. [ bib \| DOI \| pdf \| software \| Abstract ]
[3]	M. Alsalti, I. Markovsky, V. G. Lopez, and M. A. Müller. Data-based system representations from irregularly measured data. IEEE Trans. Automat. Contr., 2024. [ bib \| DOI \| pdf ]
[4]	I. Markovsky, M. Alsalti, V. G. Lopez, and M. A. Müller. Identification from data with periodically missing output samples. Automatica, 169:111869, 2024. [ bib \| DOI \| pdf ]
[5]	J. Wang, L. Hemelhof, I. Markovsky, and P. Patrinos. A trust-region method for data-driven iterative learning control of nonlinear systems. Control Systems Letters, 8:1847--1852, 2024. [ bib \| DOI \| pdf ]
[6]	F. Dörfler, J. Coulson, and I. Markovsky. Bridging direct & indirect data-driven control formulations via regularizations and relaxations. IEEE Trans. Automat. Contr., 68:883--897, 2023. [ bib \| DOI \| pdf \| Abstract ]
[7]	I. Markovsky. Data-driven simulation of generalized bilinear systems via linear time-invariant embedding. IEEE Trans. Automat. Contr., 68:1101--1106, 2023. [ bib \| DOI \| pdf \| software \| Abstract ]
[8]	I. Markovsky, E. Prieto-Araujo, and F. Dörfler. On the persistency of excitation. Automatica, page 110657, 2023. [ bib \| DOI \| pdf \| Abstract ]
[9]	I. Markovsky and F. Dörfler. Identifiability in the behavioral setting. IEEE Trans. Automat. Contr., 68:1667--1677, 2023. [ bib \| DOI \| pdf \| software \| Abstract ]
[10]	I. Markovsky, L. Huang, and F. Dörfler. Data-driven control based on behavioral approach: From theory to applications in power systems. IEEE Control Systems Magazine, 43:28--68, 2023. [ bib \| DOI \| pdf \| software \| Abstract ]
[11]	A. Fazzi and I. Markovsky. Distance problems in the behavioral setting. European Journal of Control, 74:100832, 2023. [ bib \| DOI \| Abstract ]
[12]	A. Fazzi and I. Markovsky. Addition and intersection of linear time-invariant behaviors. IFAC Journal of Systems and Control, 26:100233, 2023. [ bib \| DOI \| Abstract ]
[13]	I. Markovsky and F. Dörfler. Data-driven dynamic interpolation and approximation. Automatica, 135:110008, 2022. [ bib \| DOI \| pdf \| Abstract ]
[14]	A. Fazzi, B. Grossmann, G. Mercère, and I. Markovsky. Mimo system identification using common denominator and numerators with known degrees. International Journal of Adaptive Control and Signal Processing, 36(4):870--881, 2022. [ bib \| DOI \| .pdf \| Abstract ]
[15]	A. Fazzi, N. Guglielmi, and I. Markovsky. A gradient system approach for Hankel structured low-rank approximation. Linear Algebra Appl., 623:236--257, 2021. [ bib \| DOI \| pdf \| Abstract ]
[16]	V. Mishra and I. Markovsky. The set of linear time-invariant unfalsified models with bounded complexity is affine. IEEE Trans. Automat. Contr., 66:4432--4435, 2021. [ bib \| DOI \| pdf \| Abstract ]
[17]	A. Fazzi, N. Guglielmi, and I. Markovsky. Generalized algorithms for the approximate matrix polynomial GCD of reducing data uncertainties with application to MIMO system and control. J. Comput. Appl. Math., 393:113499, 2021. [ bib \| DOI \| pdf \| Abstract ]
[18]	I. Markovsky and F. Dörfler. Behavioral systems theory in data-driven analysis, signal processing, and control. Annual Reviews in Control, 52:42--64, 2021. [ bib \| DOI \| pdf \| Abstract ]
[19]	G. Q. Carapia, I. Markovsky, R. Pintelon, P. Csurcsia, and D. Verbeke. Experimental validation of a data-driven step input estimation method for dynamic measurements. IEEE Transactions on Instrumentation and Measurement, 69:4843--4851, 2020. [ bib \| DOI \| pdf \| Abstract ]
[20]	G. Q. Carapia, I. Markovsky, R. Pintelon, P. Csurcsia, and D. Verbeke. Bias and covariance of the least squares estimate in a structured errors-in-variables problem. Comput. Statist. Data Anal., 144:106893, 2020. [ bib \| DOI \| pdf \| Abstract ]
[21]	G. Q. Carapia and I. Markovsky. Input parameters estimation from time-varying measurements. Measurement, 153:107418, 2020. [ bib \| DOI \| pdf \| Abstract ]
[22]	I. Markovsky, T. Liu, and A. Takeda. Data-driven structured noise filtering via common dynamics estimation. IEEE Trans. Signal Process., 68:3064--3073, 2020. [ bib \| DOI \| pdf \| software \| Abstract ]
[23]	V. Mishra, I. Markovsky, and B. Grossmann. Data-driven tests for controllability. Control Systems Letters, 5:517--522, 2020. [ bib \| DOI \| pdf \| Abstract ]
[24]	T. Liu, I. Markovsky, T.-K. Pong, and A. Takeda. A hybrid penalty method for a class of optimization problems with multiple rank constraints. SIAM J. Matrix Anal. Appl., 41:1260--1283, 2020. [ bib \| DOI \| pdf \| Abstract ]
[25]	M. Zhang, I. Markovsky, C. Schretter, and J. D'hooge. Compressed ultrasound signal reconstruction using a low-rank and joint-sparse representation model. Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 66:1232--1245, 2019. [ bib \| DOI \| pdf \| Abstract ]
[26]	I. Markovsky. On the behavior of autonomous Wiener systems. Automatica, 110:108601, 2019. [ bib \| DOI \| pdf \| Abstract ]
[27]	A. Fazzi, N. Guglielmi, and I. Markovsky. An ODE based method for computing the approximate greatest common divisor of polynomials. Numerical algorithms, 81:719--740, 2018. [ bib \| DOI \| pdf \| Abstract ]
[28]	K. Usevich and I. Markovsky. Variable projection methods for approximate (greatest) common divisor computations. Theoretical Computer Science, 681:176--198, 2017. [ bib \| DOI \| pdf \| Abstract ]
[29]	I. Markovsky. A missing data approach to data-driven filtering and control. IEEE Trans. Automat. Contr., 62:1972--1978, 2017. [ bib \| DOI \| pdf \| software \| Abstract ]
[30]	I. Markovsky and G. Mercère. Subspace identification with constraints on the impulse response. Int. J. Contr., 90:1728--1735, 2017. [ bib \| DOI \| pdf \| software \| Abstract ]
[31]	N. Guglielmi and I. Markovsky. An ODE based method for computing the distance of co-prime polynomials to common divisibility. SIAM Journal on Numerical Analysis, 55:1456--1482, 2017. [ bib \| DOI \| pdf \| software \| Abstract ]
[32]	K. Usevich and I. Markovsky. Adjusted least squares fitting of algebraic hypersurfaces. Linear Algebra Appl., 502:243--274, 2016. [ bib \| DOI \| pdf \| Abstract ]
[33]	I. Markovsky. On the most powerful unfalsified model for data with missing values. Systems & Control Lett., 95:53--61, 2016. [ bib \| DOI \| pdf \| software \| Abstract ]
[34]	I. Markovsky. Comparison of adaptive and model-free methods for dynamic measurement. IEEE Signal Proc. Lett., 22(8):1094--1097, 2015. [ bib \| DOI \| pdf \| software \| Abstract ]
[35]	I. Markovsky and R. Pintelon. Identification of linear time-invariant systems from multiple experiments. IEEE Trans. Signal Process., 63(13):3549--3554, 2015. [ bib \| DOI \| pdf \| Abstract ]
[36]	I. Markovsky. An application of system identification in metrology. Control Eng. Practice, 43:85--93, 2015. [ bib \| DOI \| pdf \| software \| Abstract ]
[37]	K. Usevich and I. Markovsky. Variable projection for affinely structured low-rank approximation in weighted 2-norms. J. Comput. Appl. Math., 272:430--448, 2014. [ bib \| DOI \| pdf \| software \| Abstract ]
[38]	I. Markovsky and K. Usevich. Software for weighted structured low-rank approximation. J. Comput. Appl. Math., 256:278--292, 2014. [ bib \| DOI \| pdf \| software \| Abstract ]
[39]	I. Markovsky. Recent progress on variable projection methods for structured low-rank approximation. Signal Processing, 96PB:406--419, 2014. [ bib \| DOI \| pdf \| software \| Abstract ]
[40]	K. Usevich and I. Markovsky. Optimization on a Grassmann manifold with application to system identification. Automatica, 50:1656--1662, 2014. [ bib \| DOI \| pdf \| software \| Abstract ]
[41]	I. Markovsky, J. Goos, K. Usevich, and R. Pintelon. Realization and identification of autonomous linear periodically time-varying systems. Automatica, 50:1632--1640, 2014. [ bib \| DOI \| pdf \| software \| Abstract ]
[42]	M. Ishteva, K. Usevich, and I. Markovsky. Factorization approach to structured low-rank approximation with applications. SIAM J. Matrix Anal. Appl., 35(3):1180--1204, 2014. [ bib \| DOI \| pdf \| software \| Abstract ]
[43]	S. Rhode, K. Usevich, I. Markovsky, and F. Gauterin. A recursive restricted total least-squares algorithm. IEEE Trans. Signal Process., 62(21):5652--5662, 2014. [ bib \| DOI \| pdf \| software \| Abstract ]
[44]	I. Markovsky and K. Usevich. Structured low-rank approximation with missing data. SIAM J. Matrix Anal. Appl., 34(2):814--830, 2013. [ bib \| DOI \| pdf \| software \| Abstract ]
[45]	I. Markovsky. A software package for system identification in the behavioral setting. Control Eng. Practice, 21:1422--1436, 2013. [ bib \| DOI \| pdf \| software \| Abstract ]
[46]	F. Le, I. Markovsky, C. Freeman, and E. Rogers. Recursive identification of Hammerstein systems with application to electrically stimulated muscle. Control Eng. Practice, 20(4):386--396, 2012. [ bib \| DOI \| pdf \| Abstract ]
[47]	I. Markovsky. On the complex least squares problem with constrained phase. SIAM J. Matrix Anal. Appl., 32(3):987--992, 2011. [ bib \| DOI \| pdf \| software \| Abstract ]
[48]	I. Markovsky. Closed-loop data-driven simulation. Int. J. Contr., 83(10):2134--2139, 2010. [ bib \| DOI \| pdf \| software \| http \| Abstract ]
[49]	I. Markovsky, D. Sima, and S. Van Huffel. Total least squares methods. Wiley Interdisciplinary Reviews: Comput. Stat., 2(2):212--217, 2010. [ bib \| DOI \| pdf \| Abstract ]
[50]	F. Le, I. Markovsky, C. Freeman, and E. Rogers. Identification of electrically stimulated muscle models of stroke patients. Control Eng. Practice, 18(4):396--407, 2010. [ bib \| DOI \| pdf \| Abstract ]
[51]	I. Markovsky. Bibliography on total least squares and related methods. Statistics and Its Interface, 3:329--334, 2010. [ bib \| pdf \| Abstract ]
[52]	I. Markovsky and S. Mahmoodi. Least-squares contour alignment. IEEE Signal Proc. Letters, 16(1):41--44, 2009. [ bib \| DOI \| pdf \| software \| Abstract ]
[53]	I. Markovsky and P. Rapisarda. Data-driven simulation and control. Int. J. Contr., 81(12):1946--1959, 2008. [ bib \| DOI \| pdf \| Abstract ]
[54]	I. Markovsky. Structured low-rank approximation and its applications. Automatica, 44(4):891--909, 2008. [ bib \| DOI \| pdf \| software \| Abstract ]
[55]	I. Markovsky and M. Niranjan. Approximate low-rank factorization with structured factors. Comput. Statist. Data Anal., 54:3411--3420, 2008. [ bib \| DOI \| pdf \| software \| http \| Abstract ]
[56]	I. Markovsky and S. Van Huffel. Overview of total least squares methods. Signal Processing, 87:2283--2302, 2007. [ bib \| DOI \| pdf \| Abstract ]
[57]	S. Shklyar, A. Kukush, I. Markovsky, and S. Van Huffel. On the conic section fitting problem. Journal of Multivariate Analysis, 98:588--624, 2007. [ bib \| DOI \| pdf \| Abstract ]
[58]	I. Markovsky and S. Van Huffel. Left vs right representations for solving weighted low rank approximation problems. Linear Algebra Appl., 422:540--552, 2007. [ bib \| DOI \| pdf \| software \| Abstract ]
[59]	M. Schuermans, I. Markovsky, and S. Van Huffel. An adapted version of the element-wise weighted TLS method for applications in chemometrics. Chemometrics and Intelligent Laboratory Systems, 85(1):40--46, 2007. [ bib \| DOI \| pdf \| Abstract ]
[60]	A. Kukush, I. Markovsky, and S. Van Huffel. Estimation in a linear multivariate measurement error model with a change point in the data. Comput. Statist. Data Anal., 52(2):1167--1182, 2007. [ bib \| DOI \| pdf \| Abstract ]
[61]	I. Markovsky, M. Rastello, A. Premoli, A. Kukush, and S. Van Huffel. The element-wise weighted total least squares problem. Comput. Statist. Data Anal., 50(1):181--209, 2005. [ bib \| DOI \| pdf \| software \| Abstract ]
[62]	A. Kukush, I. Markovsky, and S. Van Huffel. Consistency of the structured total least squares estimator in a multivariate errors-in-variables model. J. Statist. Plann. Inference, 133(2):315--358, 2005. [ bib \| DOI \| pdf \| Abstract ]
[63]	I. Markovsky, S. Van Huffel, and R. Pintelon. Block-Toeplitz/Hankel structured total least squares. SIAM J. Matrix Anal. Appl., 26(4):1083--1099, 2005. [ bib \| DOI \| pdf \| software \| Abstract ]
[64]	I. Markovsky and B. De Moor. Linear dynamic filtering with noisy input and output. Automatica, 41(1):167--171, 2005. [ bib \| DOI \| Abstract ]
[65]	I. Markovsky, J. C. Willems, P. Rapisarda, and B. De Moor. Algorithms for deterministic balanced subspace identification. Automatica, 41(5):755--766, 2005. [ bib \| DOI \| pdf \| software \| Abstract ]
[66]	I. Markovsky, J. C. Willems, S. Van Huffel, B. De Moor, and R. Pintelon. Application of structured total least squares for system identification and model reduction. IEEE Trans. Automat. Contr., 50(10):1490--1500, 2005. [ bib \| DOI \| pdf \| software \| Abstract ]
[67]	J. C. Willems, P. Rapisarda, I. Markovsky, and B. De Moor. A note on persistency of excitation. Systems & Control Lett., 54(4):325--329, 2005. [ bib \| DOI \| pdf \| Abstract ]
[68]	I. Markovsky and S. Van Huffel. High-performance numerical algorithms and software for structured total least squares. J. Comput. Appl. Math., 180(2):311--331, 2005. [ bib \| DOI \| pdf \| software \| Abstract ]
[69]	M. Schuermans, I. Markovsky, P. Wentzell, and S. Van Huffel. On the equivalence between total least squares and maximum likelihood PCA. Analytica Chimica Acta, 544(1--2):254--267, 2005. [ bib \| DOI \| pdf \| Abstract ]
[70]	A. Kukush, I. Markovsky, and S. Van Huffel. Consistent estimation in an implicit quadratic measurement error model. Comput. Statist. Data Anal., 47(1):123--147, 2004. [ bib \| DOI \| pdf \| software \| Abstract ]
[71]	I. Markovsky, A. Kukush, and S. Van Huffel. Consistent least squares fitting of ellipsoids. Numerische Mathematik, 98(1):177--194, 2004. [ bib \| DOI \| pdf \| software \| Abstract ]
[72]	I. Markovsky, S. Van Huffel, and A. Kukush. On the computation of the structured total least squares estimator. Numer. Linear. Algebra Appl., 11:591--608, 2004. [ bib \| DOI \| pdf \| software \| Abstract ]
[73]	A. Kukush, I. Markovsky, and S. Van Huffel. Consistent estimation in the bilinear multivariate errors-in-variables model. Metrika, 57(3):253--285, 2003. [ bib \| DOI \| pdf \| Abstract ]
[74]	A. Kukush, I. Markovsky, and S. Van Huffel. Consistent fundamental matrix estimation in a quadratic measurement error model arising in motion analysis. Comput. Statist. Data Anal., 41(1):3--18, 2002. [ bib \| DOI \| pdf \| Abstract ]
[75]	M. Lemmon, K. He, and I. Markovsky. Supervisory hybrid systems. IEEE Control Systems Magazine, 19(4):42--55, August 1999. [ bib \| DOI \| Abstract ]
[76]	A. Fazzi, K. Usevich, and I. Markovsky. Implementation improvements and extensions of an ode-based algorithm for structured low-rank approximation. Calcolo. [ bib ]

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