Academic literature on the topic 'Yule-Walker equations'

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Journal articles on the topic "Yule-Walker equations"

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Kallas, M., P. Honeine, C. Francis, and H. Amoud. "Kernel autoregressive models using Yule–Walker equations." Signal Processing 93, no. 11 (November 2013): 3053–61. http://dx.doi.org/10.1016/j.sigpro.2013.03.032.

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Davila, C. E. "On the noise-compensated Yule-Walker equations." IEEE Transactions on Signal Processing 49, no. 6 (June 2001): 1119–21. http://dx.doi.org/10.1109/78.923293.

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Martinelli, G., G. Orlandi, and P. Burrascano. "Yule - Walker equations and Bartlett's bisection theory." IEEE Transactions on Circuits and Systems 32, no. 10 (October 1985): 1074–76. http://dx.doi.org/10.1109/tcs.1985.1085615.

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Hui-Min Zhang and P. Duhamel. "On the methods for solving Yule-Walker equations." IEEE Transactions on Signal Processing 40, no. 12 (1992): 2987–3000. http://dx.doi.org/10.1109/78.175742.

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Wang, Hai-Bin, and Bo-Cheng Wei. "Separable lower triangular bilinear model." Journal of Applied Probability 41, no. 01 (March 2004): 221–35. http://dx.doi.org/10.1017/s0021900200014169.

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The aim of this paper is to analyze the probabilistic structure for a rather general class of bilinear models systematically. First, the sufficient and necessary conditions for stationarity are given with a concise expression. Then both the autocovariance function and the spectral density function are obtained. The Yule–Walker-type difference equations for autocovariances are derived by means of the spectral density function. Concerning the second-order probabilistic structure, the model is similar to an ARMA model. The third-order probabilistic structure for the model is discussed and a group of Yule–Walker-type difference equations for third-order cumulants are discovered.
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Wang, Hai-Bin, and Bo-Cheng Wei. "Separable lower triangular bilinear model." Journal of Applied Probability 41, no. 1 (March 2004): 221–35. http://dx.doi.org/10.1239/jap/1077134680.

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The aim of this paper is to analyze the probabilistic structure for a rather general class of bilinear models systematically. First, the sufficient and necessary conditions for stationarity are given with a concise expression. Then both the autocovariance function and the spectral density function are obtained. The Yule–Walker-type difference equations for autocovariances are derived by means of the spectral density function. Concerning the second-order probabilistic structure, the model is similar to an ARMA model. The third-order probabilistic structure for the model is discussed and a group of Yule–Walker-type difference equations for third-order cumulants are discovered.
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Dimitriou-Fakalou, Chrysoula. "Yule-Walker Estimation for the Moving-Average Model." International Journal of Stochastic Analysis 2011 (August 14, 2011): 1–20. http://dx.doi.org/10.1155/2011/151823.

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The standard Yule-Walker equations, as they are known for an autoregression, are generalized to involve the moments of a moving-average process indexed on any number of dimensions. Once observations become available, new moments estimators are set to imitate the theoretical equations. These estimators are not only consistent but also asymptotically normal for any number of indexes. Their variance matrix resembles a standard result from maximum Gaussian likelihood estimation. A simulation study is added to conclude on their efficiency.
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Vergara-Dominguez, L. "New insights into the high-order Yule-Walker equations." IEEE Transactions on Acoustics, Speech, and Signal Processing 38, no. 9 (1990): 1649–51. http://dx.doi.org/10.1109/29.60088.

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Chen, Weitian, Brian D. O. Anderson, Manfred Deistler, and Alexander Filler. "Solutions of Yule-Walker equations for singular AR processes." Journal of Time Series Analysis 32, no. 5 (January 27, 2011): 531–38. http://dx.doi.org/10.1111/j.1467-9892.2010.00711.x.

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Tismenetsky, Miron. "Some properties of solutions of Yule-Walker type equations." Linear Algebra and its Applications 173 (August 1992): 1–17. http://dx.doi.org/10.1016/0024-3795(92)90419-b.

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Dissertations / Theses on the topic "Yule-Walker equations"

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Illig, Aude. "Etude asymptotique de certains estimateurs pour des modèles ARMA spatiaux." Toulouse, INSA, 2004. http://www.theses.fr/2004ISAT0027.

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Nous nous intéressons à l'étude asymptotique de certaines statistiques dans des modèles ARMA spatiaux quadrantaux dont les innovations sont supposées être indépendantes et identiquement distribuées ou plus généralement vérifier une propriété de martingales fortes. Après une revue des théorèmes limites pour des martingales spatiales sur un réseau, nous démontrons d'abord un théorème de la limite centrale et un principe d'invariance sous la condition de Lindeberg conditionnelle pour des tableaux de martingales fortes. Afin de mieux situer notre étude des champs ARMA quadrantaux, nous rappelons divers résultats conçernant l'estimation et l'identification dans d'autres modèles ARMA spatiaux. Puis, dans le but de sélectionner les ordres et d'estimer les paramètresautorégressifs de modèles ARMA spatiaux quadrantaux, nous introduisons un nouvel estimateur obtenu à partir des équations de Yule-Walker généralisées. Nous démontrons sa consistance et sa normalité asymptotique. Enfin, pour un certain nombre de modèles ARMA spatiaux, nous illustrons leurs comportements par des représentations graphiques et nous présentons une étude de procédures pour les identifier à partir de nombreuses simulations
We study the asymptotic behaviour of some statistics for spatial quadrantal ARMA models with independent and identically distributed innovations or more generally strong martingales innovations. After giving a review of limit theorems for lattice martingales, we establish a central limit theorem and an invariance principle under the conditional Lindeberg condition for strong latticemartingales. For a better understanding of our study on quadrantal ARMA random fields, we recall various results on estimation and identification for others spatial ARMA models. Then, in order to select orders and estimate autoregressive coefficients in spatial quadrantal ARMA models, we introduce a new estimator based on a derivation of extended Yule-Walker equations and prove that it is consistent and asymptotically normal. Finally, we illustrate with graphic representations the behaviour of several spatial ARMA models and present a study of procedures for their identification
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Kayahan, Gurhan. "ARMA modeling." Thesis, 1988. http://hdl.handle.net/10945/22912.

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Approved for public release; distribution is unlimited
This thesis estimates the frequency response of a network where the only data is the output obtained from an Autoregressive-moving average (ARMA) model driven by a random input. Models of random processes and existing methods for solving ARMA models are examined. The estimation is performed iteratively by using the Yule-Walker Equations in three different methods for the AR part and the Cholesky factorization for the MA part. The AR parameters are estimated initially, then MA parameters are estimated assuming that the AR parameters have been compensated for. After the estimation of each parameter set, the original time series is filtered via the inverse of the last estimate of the transfer function of an AR model or MA model, allowing better and better estimation of each model's coefficients. The iteration refers to the procedure of removing the MA or AR part from the random process in an alternating fashion allowing the creation of an almost pure AR or MA process, respectively. As the iteration continues the estimates are improving. When the iteration reaches a point where the coefficients converse the last VIA and AR model coefficients are retained as final estimates.
http://archive.org/details/armamodeling00kaya
Lieutenant Junior Grade, Turkish Navy
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Hsu, Jer-Wei, and 許哲維. "Design and Implementation of Multi-Band/Multi-Level Microwave Filters Using Discrete-Time Techniques and Yule-Walker Equation." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/42729408078513718683.

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碩士
國立臺灣科技大學
電子工程系
96
The modified Yule-Walker equation is commonly used for estimating the time domain parameters of an autoregressive process. In this thesis, we propose a new method to design microwave filters. We employ the modified Yule-Walker equation to obtain discrete-time filters having multi-band and/or multi-level responses. We apply this new method to achieve a single-band bandpass filter, a dual-band bandpass filter, and a two-level bandpass filter. At last, we change the normalized frequency of two-section shunt-open stub, which produce attenuation pole in higher band, to make the size be smaller. In this method, the system functions of bandpass filters in Z-domain are studied first. The Z-domain chain-scattering matrices of transmission lines and the modified Yule-Walker equation are then derived in the thesis. The filters are implemented with serial lines, open-circuited single-section stubs, short-circuited single-section stubs, and open-circuited two-section stubs. Experimental results are presented to illustrate the validity of this design method.
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Book chapters on the topic "Yule-Walker equations"

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"The Yule-Walker Equations and the Partial Autocorrelation Function." In Basic Data Analysis for Time Series with R, 169–79. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118593233.ch15.

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Conference papers on the topic "Yule-Walker equations"

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Gohberg, I., Israel Koltracht, and Tongsan D. Xiao. "Solution of the Yule-Walker equations." In San Diego, '91, San Diego, CA, edited by Franklin T. Luk. SPIE, 1991. http://dx.doi.org/10.1117/12.49808.

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Kallas, Maya, Clovis Francis, Paul Honeine, Hassan Amoud, and Cedric Richard. "Modeling electrocardiogram using Yule-Walker equations and kernel machines." In 2012 19th International Conference on Telecommunications (ICT). IEEE, 2012. http://dx.doi.org/10.1109/ictel.2012.6221217.

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Kallas, Maya, Paul Honeine, Cedric Richard, Clovis Francis, and Hassan Amoud. "Prediction of time series using Yule-Walker equations with kernels." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6288346.

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Porsani, Milton, Tad J. Ulrych, Jonilton Pessoa, W. Scott, P. Leaney, and Oliver G. Jensen. "Extended Yule‐Walker equations, nonwhite deconvolution and roots of polynomials." In SEG Technical Program Expanded Abstracts 1989. Society of Exploration Geophysicists, 1989. http://dx.doi.org/10.1190/1.1889538.

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Libal, Urszula, and Karl H. Johansson. "Yule-Walker Equations Using Higher Order Statistics for Nonlinear Autoregressive Model." In 2019 Signal Processing Symposium (SPSympo). IEEE, 2019. http://dx.doi.org/10.1109/sps.2019.8882057.

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Soverini, Umberto, and Torsten Soderstrom. "Frequency domain EIV identification combining the Frisch scheme and Yule-Walker equations." In 2015 European Control Conference (ECC). IEEE, 2015. http://dx.doi.org/10.1109/ecc.2015.7330839.

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Yeredor, Arie. "Yule-Walker Equations Applied to Hessians of the Characteristic Function for Improved AR Estimation." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.366857.

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Péntek, Áron, and James B. Kadtke. "Detection of Low-SNR Signals Using Dynamical Models Estimated With Higher-Order Data Moments." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/vib-8366.

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Abstract We discuss a method for estimating nonlinear dynamical models from data, to construct a closed-form, few-parameter representation. The model coefficients are estimated using a Yule-Walker type equation involving higher-order correlations, which provides computational speed, numerical stability and noise robustness. We implement a multi-feature detector based on Mahalanobis distance and compute ROC curves for several signal classes. We show that the dynamical detectors’ performance can be improved significantly by increasing the sampling rate.
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Mossberg, M. "Pole estimation by the Yule-Walker equation and the total least squares algorithm." In 2006 American Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/acc.2006.1656461.

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Reports on the topic "Yule-Walker equations"

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Kay, Steven M., and Christopher P. Carbone. Vector Space Solution to the Multidimensional Yule-Walker Equations. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada523725.

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