{"_id":"40759","place":"Pacific Grove, USA","year":"2014","citation":{"bibtex":"@inproceedings{Ramírez_Schreier_Vía_Santamaría_Scharf_2014, place={Pacific Grove, USA}, title={A Regularized Maximum Likelihood Estimator for the Period of a Cyclostationary Process}, booktitle={Proc.\\ Asilomar Conf.\\ Signals Syst.\\ Computers}, author={Ramírez, D. and Schreier, P. J. and Vía, J. and Santamaría, I. and Scharf, L. L.}, year={2014} }","ama":"Ramírez D, Schreier PJ, Vía J, Santamaría I, Scharf LL. A Regularized Maximum Likelihood Estimator for the Period of a Cyclostationary Process. In: <i>Proc.\\ Asilomar Conf.\\ Signals Syst.\\ Computers</i>. ; 2014.","apa":"Ramírez, D., Schreier, P. J., Vía, J., Santamaría, I., &#38; Scharf, L. L. (2014). A Regularized Maximum Likelihood Estimator for the Period of a Cyclostationary Process. <i>Proc.\\ Asilomar Conf.\\ Signals Syst.\\ Computers</i>.","ieee":"D. Ramírez, P. J. Schreier, J. Vía, I. Santamaría, and L. L. Scharf, “A Regularized Maximum Likelihood Estimator for the Period of a Cyclostationary Process,” 2014.","short":"D. Ramírez, P.J. Schreier, J. Vía, I. Santamaría, L.L. Scharf, in: Proc.\\ Asilomar Conf.\\ Signals Syst.\\ Computers, Pacific Grove, USA, 2014.","mla":"Ramírez, D., et al. “A Regularized Maximum Likelihood Estimator for the Period of a Cyclostationary Process.” <i>Proc.\\ Asilomar Conf.\\ Signals Syst.\\ Computers</i>, 2014.","chicago":"Ramírez, D., P. J. Schreier, J. Vía, I. Santamaría, and L. L. Scharf. “A Regularized Maximum Likelihood Estimator for the Period of a Cyclostationary Process.” In <i>Proc.\\ Asilomar Conf.\\ Signals Syst.\\ Computers</i>. Pacific Grove, USA, 2014."},"abstract":[{"text":"We derive an estimator of the cycle period of a univariate cyclostationary process based on an information- theoretic criterion. Transforming the univariate cyclostationary process into a vector-valued wide-sense stationary process allows us to obtain the structure of the covariance matrix, which is block-Toeplitz, and its block size depends on the unknown cycle period. Therefore, we sweep the block size and obtain the ML estimate of the covariance matrix, required for the information- theoretic criterion. Since there are no closed-form ML estimates of block-Toeplitz matrices, we asymptotically approximate them as block-circulant. Finally, some numerical examples show the good performance of the proposed estimator.","lang":"eng"}],"date_created":"2023-01-30T11:51:48Z","publication":"Proc.\\ Asilomar Conf.\\ Signals Syst.\\ Computers","author":[{"last_name":"Ramírez","first_name":"D.","full_name":"Ramírez, D."},{"first_name":"P. J.","last_name":"Schreier","full_name":"Schreier, P. J."},{"last_name":"Vía","first_name":"J.","full_name":"Vía, J."},{"full_name":"Santamaría, I.","first_name":"I.","last_name":"Santamaría"},{"full_name":"Scharf, L. L.","last_name":"Scharf","first_name":"L. L."}],"status":"public","date_updated":"2023-01-30T12:00:37Z","title":"A Regularized Maximum Likelihood Estimator for the Period of a Cyclostationary Process","user_id":"43497","type":"conference","department":[{"_id":"263"}]}