Academic literature on the topic 'Yule-Walker Method'

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Journal articles on the topic "Yule-Walker Method"

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Jansson, Magnus, and Petre Stoica. "Optimal Yule Walker Method for Pole Estimation of ARMA Signals." IFAC Proceedings Volumes 36, no. 16 (2003): 1891–95. http://dx.doi.org/10.1016/s1474-6670(17)35036-x.

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Friedlander, Benjamin, and Boaz Porat. "Multichannel arma spectral estimation by the modified Yule-Walker method." Signal Processing 10, no. 1 (1986): 49–59. http://dx.doi.org/10.1016/0165-1684(86)90064-2.

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Mahmoudi, Alimorad. "Adaptive Algorithm for Estimation of Two-Dimensional Autoregressive Fields from Noisy Observations." International Journal of Stochastic Analysis 2014 (December 25, 2014): 1–5. http://dx.doi.org/10.1155/2014/247274.

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This paper deals with the problem of two-dimensional autoregressive (AR) estimation from noisy observations. The Yule-Walker equations are solved using adaptive steepest descent (SD) algorithm. Performance comparisons are made with other existing methods to demonstrate merits of the proposed method.
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Nurhidayati, Maulida. "Estimasi Parameter Model Autoregressive dengan Metode Yule Walker, Least Square, dan Maximum Likelihood (Studi Kasus Data ROA BPRS di Indonesia)." Quadratic: Journal of Innovation and Technology in Mathematics and Mathematics Education 1, no. 1 (2021): 1–6. http://dx.doi.org/10.14421/quadratic.2021.011-01.

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The Autoregressive model is a time series univariate model for stationary models. In estimating parameters on this model can be done by several methods, namely yule-walker method, Least Square, and Maximum Likelihood. Each method has a different principle for estimating model parameters so that the results obtained will also be different. Based on this, in this study, the AR(1) model parameter estimation was estimated by generating data simulated 1000 times to see the performance of Yule-Walker, Least Square, and Maximum Likelihood methods. In addition, the comparison of these three methods is also done on ROA BPRS data that follows the AR(1) model. The results showed that the Maximum Likelihood method was able to provide mode results and comparison of the most suitable estimation results for simulation data and produce the smallest MAE values in the data in sample and MAPE, MSE, and MAE the smallest in the out sample data. These results show that the Maximum Likelihood method is the best method for modeling data that follows the AR(1) model.
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Kruczek, Piotr, Agnieszka Wyłomańska, Marek Teuerle та Janusz Gajda. "The modified Yule-Walker method for α-stable time series models". Physica A: Statistical Mechanics and its Applications 469 (березень 2017): 588–603. http://dx.doi.org/10.1016/j.physa.2016.11.037.

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Wang, Bin, YanPing Wang, Wen Hong, WeiXian Tan, and YiRong Wu. "Studies on MB-SAR 3D imaging algorithm using Yule-Walker method." Science China Information Sciences 53, no. 9 (2010): 1848–59. http://dx.doi.org/10.1007/s11432-010-4040-7.

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Paun, Mihaela, Nevine Gunaime, and Bogdan M. Strimbu. "Impact of Algorithm Selection on Modeling Ozone Pollution: A Perspective on Box and Tiao (1975)." Forests 11, no. 12 (2020): 1311. http://dx.doi.org/10.3390/f11121311.

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Estimation using a suboptimal method can lead to imprecise models, with cascading effects in complex models, such as climate change or pollution. The goal of this study is to compare the solutions supplied by different algorithms used to model ozone pollution. Using Box and Tiao (1975) study, we have predicted ozone concentration in Los Angeles with an ARIMA and an autoregressive process. We have solved the ARIMA process with three algorithms (i.e., maximum likelihood, like Box and Tiao, conditional least square and unconditional least square) and the autoregressive process with four algorithms (i.e., Yule–Walker, iterative Yule–Walker, maximum likelihood, and unconditional least square). Our study shows that Box and Tiao chose the appropriate algorithm according to the AIC but not according to the mean square error. Furthermore, Yule–Walker, which is the default algorithm in many software, has the least reliable results, suggesting that the method of solving complex models could alter the findings. Finally, the model selection depends on the technical details and on the applicability of the model, as the ARIMA model is suitable from the AIC perspective but an autoregressive model could be preferred from the mean square error viewpoint. Our study shows that time series analysis should consider not only the model shape but also the model estimation, to ensure valid results.
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Broersen, P. "Finite-Sample Bias Propagation in Autoregressive Estimation With the Yule–Walker Method." IEEE Transactions on Instrumentation and Measurement 58, no. 5 (2009): 1354–60. http://dx.doi.org/10.1109/tim.2008.2009400.

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Hsue, Ching-Wen, Jer-Wei Hsu, Yen-Jen Chen, and Kuo-Lung Chen. "Microwave multi-level band-pass filter using discrete-time Yule-Walker method." Microwave and Optical Technology Letters 51, no. 1 (2008): 225–29. http://dx.doi.org/10.1002/mop.24006.

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Broersen, Piet M. T. "Finite-Sample Bias Propagation in the Yule-Walker Method of Autoregressive Estimation." IFAC Proceedings Volumes 41, no. 2 (2008): 2744–49. http://dx.doi.org/10.3182/20080706-5-kr-1001.00462.

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

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Dulesov, Egor. "Identifikace významných spektrálních složek ve stresovém řečovém signálu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-241000.

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The aim of this master’s thesis is to learn the problem of analysis and identification of significant spectral components in speech signal. Based on learning a special literature chooses the suitable methods of spectrum estimate. Does learning the literature in specification of testing of spectral components significate. Makes a procedure for identification of chosen speech formants. Does this procedure for audio signals both of in stress and in normal state. Estimates the results, compares efficiency of chosen methods and determine threshold for chosen formant of analyzed stress signal. States the recommendations for speech spectral analysis in stress situation.
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Pospíšil, Lukáš. "Analýza ROC křivek zvukových signálů a jejich srovnání." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-316445.

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This thesis deals with oportunity of ROC curve usage in the description of methods that work with sound signals. Specifically, it focuses on ways of detecting of stress in speech signals. The detection itselfs is done in a range of frequencies of the sound signal. There is also a classifier designed using ROC curves that decides whether the input signal is stressed or not. The output of this thesis are findings gathered from analyses and also some recommendation based on those analyses.
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Book chapters on the topic "Yule-Walker Method"

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Radha, V., C. Vimala, and M. Krishnaveni. "Power Spectral Density Estimation Using Yule Walker AR Method for Tamil Speech Signal." In Information Systems for Indian Languages. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19403-0_49.

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Yavuz, Erdem, and Vedat Topuz. "Recognition of Turkish Vowels by Probabilistic Neural Networks Using Yule-Walker AR Method." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13769-3_14.

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Slowik, Adam. "Hybridization of Evolutionary Algorithm with Yule Walker Method to Design Minimal Phase Digital Filters with Arbitrary Amplitude Characteristics." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21219-2_10.

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Chen, Baoline, and Peter A. Zadrozny. "AN EXTENDED YULE-WALKER METHOD FOR ESTIMATING A VECTOR AUTOREGRESSIVE MODEL WITH MIXED-FREQUENCEY DATA." In Advances in Econometrics. Emerald Group Publishing Limited, 1999. http://dx.doi.org/10.1108/s0731-9053(1999)0000013005.

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Conference papers on the topic "Yule-Walker Method"

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Broersen, Piet M. T. "Finite-Sample Bias in the Yule-Walker Method of Autoregressive Estimation." In 2008 IEEE Instrumentation and Measurement Technology Conference - I2MTC 2008. IEEE, 2008. http://dx.doi.org/10.1109/imtc.2008.4547058.

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Follum, Jim, Tamara Becejac, and Pavel Etingov. "A Robust Yule-Walker Method for Online Monitoring of Power System Electromechanical Modes of Oscillation." In 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). IEEE, 2021. http://dx.doi.org/10.1109/isgt49243.2021.9372152.

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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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Soderstrom, T., and P. Stoica. "On the accuracy of high-order Yule-Walker methods for cisoids." In [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1991. http://dx.doi.org/10.1109/icassp.1991.150247.

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