Full Text

See more details

X (3)

Mendeley (7)

16

CITATIONS

16 Total citations

4 Recent citations

5.3 Field Citation Ratio

n/a Relative Citation Ratio

publications

0

supporting

0

mentioning

0

contrasting

0

Smart Citations

0

0

0

0

Citing PublicationsSupportingMentioningContrasting

View Citations

See how this article has been cited at scite.ai

scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

• H. Akaike. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19:716-723, 1974.
• A. Aravkin, J. V. Burke, A. Chiuso, and G. Pillonetto. Convex vs non-convex estimators for regression and sparse estimation: the mean squared error properties of ard and glasso. Journal of Machine Learning Research, 15:217-252, 2014.
• W.W. Barrett, C.R. Johnson, and M. Lundquist. Determinantal formulation for matrix completions associated with chordal graphs. Linear Algebra and its Applications, 121:265-289, 1989.
• J. O. Berger. Statistical decision theory and Bayesian analysis. Springer Verlag, 1985.
• F. P. Carli, T. Chen, A. Chiuso, L. Ljung, and G. Pillonetto. On the estimation of hyperparameters for bayesian system identification with exponentially decaying kernels. In Decision and Control (CDC), 2012 IEEE 51st Annual Conference on, pages 5260-5265. IEEE, 2012.
• F. P. Carli, A. Chiuso, and G. Pillonetto. Efficient algorithms for large scale linear system identification using stable spline estimators. In Proceedings of the 16th IFAC symposium on system identification, SysId 2012, pages 119-124. IFAC, 2012.
• F.P. Carli, A. Ferrante, M. Pavon, and G. Picci. A maximum entropy solution of the covariance extension problem for reciprocal processes. IEEE Transactions on Automatic Control, 56(9):1999-2012, 2011.
• F.P. Carli, A. Ferrante, M. Pavon, and G. Picci. An efficient algorithm for maximum entropy extension of block-circulant covariance matrices. Linear Algebra and its Applications, 439(8):2309-2329, 2013.
• F.P. Carli and T.T. Georgiou. On the covariance completion problem under a circulant structure. IEEE Transactions on Automatic Control, 56(4):918-922, 2011.
• T. Chen and L. Ljung. Implementation of algorithms for tuning parameters in regularized least squares problems in system identification. Automatica, 49(7):2213-2220, 2013.
• T. Chen, H. Ohlsson, G. C. Goodwin, and L. Ljung. Kernel selection in linear system identification part II: A classical perspective. In Decision and Control and European Control Conference (CDC-ECC), 2011 50th IEEE Conference on, pages 4326-4331. IEEE, 2011.
• T. Chen, H. Ohlsson, and L. Ljung. On the estimation of transfer functions, regularizations and Gaussian processes-revisited. Automatica, 48(8):1525-1535, 2012.
• J. Dahl, L. Vanderberghe, and V. Roychowdhury. Covariance selection for non-chordal graphs via chordal embedding. Optimization Methods and Software, 23:501-520, 2008.
• A. P. Dempster. Covariance selection. Biometrics, 28:157-175, 1972.
• H. Dym and I. Gohberg. Extensions of band matrices with band inverses. Linear algebra and its applications, 36:1-24, 1981.
• M. Fukuda, M. Kojima, K. Murota, and K. Nakata. Exploiting sparsity in semidefinite programming via matrix completion i: general framework. SIAM Journal on Optimization, 11:647-674, 2000.
• I. Gohberg, S. Goldberg, and M. A. Kaashoek. Classes of Linear Operators, II,. Birkhaüser, Basel, 1993.
• M. Golumbic. Algorithmic Graph Theory and Perfect Graphs. Academic Press, New York, 1980.
• R. Grone, C.R. Johnson, E.M. Sa, and H. Wolkowicz. Positive definite completions of partial Hermitian matrices. Linear Algebra and Its Applications, 58:109-124, 1984.
• T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning. Springer, 2008.
• C.R. Johnson. Matrix completion problems: a survey. In Proceedings of Symposia in Applied Mathematics (1990), volume 40, pages 171-198.
• L. Ljung. System Identification-Theory For the User. Prentice Hall, 1999.
• J. S. Maritz and T. Lwin. Empirical Bayes methods. Chapman and Hall London, 1989.
• K. Nakata, K. Fujitsawa, M. Fukuda, M. Kojima, and K. Murota. Exploiting sparsity in semidefinite programming via matrix completion ii: implementation and numerical details. Mathematical Programming Series B, 95:303-327, 2003.
• G. Pillonetto, A. Chiuso, and G. De Nicolao. Prediction error identification of linear systems: A nonparametric Gaussian regression approach. Automatica, 47:291-305, 2011.
• G. Pillonetto, A. Chiuso, and G. De Nicolao. Regularized estimation of sums of exponentials in spaces generated by stable spline kernels. In American Control Conference (ACC), 2010, pages 498-503. IEEE, 2010.
• G. Pillonetto and G. De Nicolao. A new kernel-based approach for linear system identification. Automatica, 46:81-93, 2010.
• G. Pillonetto and G. De Nicolao. Kernel selection in linear system identification Part I: A Gaussian process perspective. In Decision and Control and European Control Conference (CDC-ECC), 2011 50th IEEE Conference on, pages 4318-4325. IEEE, 2011.
• C. Rasmussen and C. Williams. Gaussian processes for machine learning. MIT press Cambridge, MA, 2006.
• B. Scholkopf and A. J. Smola. Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press, 2001.
• G. Schwarz. Estimating the dimension of a model. The annals of statistics, 6(2):461-464, 1978.
• J. Shawe-Taylor and N. Cristianini. Kernel methods for pattern analysis. Cambridge University Press, 2004.
• T. Soderstrom and P. Stoica. System Identification. Prentice Hall, 1989.