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Relevant bibliographies by topics / Log periodogram

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Academic literature on the topic 'Log periodogram'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Log periodogram"

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Velasco, Carlos. "NON-GAUSSIAN LOG-PERIODOGRAM REGRESSION." Econometric Theory 16, no. 1 (2000): 44–79. http://dx.doi.org/10.1017/s0266466600161031.

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We show the consistency of the log-periodogram regression estimate of the long memory parameter for long range dependent linear, not necessarily Gaussian, time series when we make a pooling of periodogram ordinates. Then, we study the asymptotic behavior of the tapered periodogram of long range dependent time series for frequencies near the origin, and we obtain the asymptotic distribution of the log-periodogram estimate for possibly non-Gaussian observation when the tapered periodogram is used. For these results we rely on higher order asymptotic properties of a vector of periodogram ordinates of the linear innovations. Finally, we assess the validity of the asymptotic results for finite samples via Monte Carlo simulation.

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Nam, Gilnam, Sinsup Cho, and In-Kwon Yeo. "Bootstrapping Log Periodogram Regression." Communications for Statistical Applications and Methods 10, no. 3 (2003): 1047–56. http://dx.doi.org/10.5351/ckss.2003.10.3.1047.

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SHIMOTSU, KATSUMI, and PETER C. B. PHILLIPS. "Pooled Log Periodogram Regression." Journal of Time Series Analysis 23, no. 1 (2002): 57–93. http://dx.doi.org/10.1111/1467-9892.00575.

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Velasco, Carlos. "Non-stationary log-periodogram regression." Journal of Econometrics 91, no. 2 (1999): 325–71. http://dx.doi.org/10.1016/s0304-4076(98)00080-3.

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Arteche, J., and J. Orbe. "Bootstrapping the log-periodogram regression." Economics Letters 86, no. 1 (2005): 79–85. http://dx.doi.org/10.1016/j.econlet.2004.06.011.

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Phillips, Peter C. B. "Unit root log periodogram regression." Journal of Econometrics 138, no. 1 (2007): 104–24. http://dx.doi.org/10.1016/j.jeconom.2006.05.017.

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Neagu, Radu, and Igor Zurbenko. "Algorithm for adaptively smoothing the log-periodogram." Journal of the Franklin Institute 340, no. 2 (2003): 103–23. http://dx.doi.org/10.1016/s0016-0032(03)00014-0.

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Davidson, James, and Philipp Sibbertsen. "Tests of bias in log-periodogram regression." Economics Letters 102, no. 2 (2009): 83–86. http://dx.doi.org/10.1016/j.econlet.2008.11.020.

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Hurvich, Clifford M., and Philippe Soulier. "TESTING FOR LONG MEMORY IN VOLATILITY." Econometric Theory 18, no. 6 (2002): 1291–308. http://dx.doi.org/10.1017/s0266466602186014.

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We consider the asymptotic behavior of log-periodogram regression estimators of the memory parameter in long-memory stochastic volatility models, under the null hypothesis of short memory in volatility. We show that in this situation, if the periodogram is computed from the log squared returns, then the estimator is asymptotically normal, with the same asymptotic mean and variance that would hold if the series were Gaussian. In particular, for the widely used GPH estimator [d with circumflex above]GPH under the null hypothesis, the asymptotic mean of m1/2[d with circumflex above]GPH is zero and the asymptotic variance is π2/24 where m is the number of Fourier frequencies used in the regression. This justifies an ordinary Wald test for long memory in volatility based on the log periodogram of the log squared returns.

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Feng, Yuanhua, and Jan Beran. "Filtered Log-Periodogram Regression of Long Memory Processes." Journal of Statistical Theory and Practice 3, no. 4 (2009): 777–93. http://dx.doi.org/10.1080/15598608.2009.10411959.

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Dissertations / Theses on the topic "Log periodogram"

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Zhou, Yinghui. "Estimating the fractional differencing parameter, d, of a long memory time series and simulating stationary and invertible time series." Thesis, University of East Anglia, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323223.

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Hardouin, Cécile. "Quelques resultats nouveaux en statistique des processus : contraste fort, regression a residus a longue portee, estimation par log-periodogramme." Paris 7, 1992. http://www.theses.fr/1992PA077084.

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On etudie d'abord les estimateurs definis par processus de contraste fort; le resultat de consistance forte est generalise au cas non ergodique: variables aleatoires independantes non identiquement distribuees, regression a residus ergodiques correles, estimation par pseudovraisemblance et par codage pour un champ markovien; la normalite asymptotique permet d'obtenir un test de melange de chi-2 de sous-hypotheses emboitees, qui devient un test du chi-2 de difference de codage dans le cas markovien. Ces resultats permettent d'etablir la consistance forte de l'estimateur des moindres carres dans le cas d'une regression a residus a longue portee, le regresseur pouvant etre deterministe ou stochastique (ergodique). Deux procedures d'estimation sont proposees, estimation conjointe ou en deux etapes, et la normalite asymptotique est obtenue via les resultats nouveaux de surgailis et giraitis etablis pour un processus lineaire. Dans le cadre d'un modele stationnaire, a longue portee ou non, gaussien ou non, on etudie ensuite l'estimation par regression sur le log-periodogramme lorsqu'une approximation de celui-ci lui est substituee. La meme procedure est developpee pour le periodogramme regularise ou l'on considere les moyennes du periodogramme etablies sur des paquets d'observations. Apres avoir montre la normalite asymptotique de ces estimateurs, nous comparons ces methodes avec celle de whittle. Finalement, des etudes de simulation permettent de confirmer les resultats theoriques obtenus: test de difference de codage pour un champ markovien, estimation de processus fractionnaires, discrimination d'un processus a longue portee et d'un ar(1)

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Books on the topic "Log periodogram"

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Wright, Jonathan H. Log-periodogram estimation of long memory volatility dependencies with conditionally heavy tailed returns. Federal Reserve Board, 2000.

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Book chapters on the topic "Log periodogram"

1

"Properties of the DFT and the Periodogram." In Large Sample Inference for Long Memory Processes. IMPERIAL COLLEGE PRESS, 2012. http://dx.doi.org/10.1142/9781848162792_0005.

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Conference papers on the topic "Log periodogram"

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Yoshida, Hisashi, Isao Fujimoto, and Sho Kikkawa. "A spectral estimation method by non-equinterval smoothing of log periodogram." In Optical Engineering + Applications, edited by Franklin T. Luk. SPIE, 2008. http://dx.doi.org/10.1117/12.801741.

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Deng, Lu, and Yingruolan Li. "Bandwidth choice of log-periodogram estimators with short-term noise in long memory series." In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6019756.

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Ko, Marwin, Brandon Stark, Monica Barbadillo, and YangQuan Chen. "An Evaluation of Three Approaches Using Hurst Estimation to Differentiate Between Normal and Abnormal HRV." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46966.

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In this study, three different approaches using seven Hurst estimators to analyze heart rate variability (HRV) are evaluated. Herein, normal sinus rhythm and arrhythmia will be referred to as normal and abnormal HRV, respectively. The Hurst parameter is estimated using the following methods: aggregated variance, absolute value, box periodogram, difference variance, Higuchi, Peng, and rescaled range [1,2]. In this paper, the three approaches used are total time series estimation, cumulative window estimation, and sliding window estimation. These approaches were influenced by previous studies [3–5]. In all three approaches, bilateral results indicate that both normal and abnormal HRV data exhibit long range dependence (LRD), when H > 0.5 [6,7]. However, normal HRV data displayed a noticeably higher amount of LRD. In this novel study, the results display further potential research avenues using Hurst parameter estimation to analyze HRV data to differentiate between normal and abnormal HRV.

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Sheng, Hu, and YangQuan Chen. "Robustness Analysis of the Estimators for Noisy Long-Range Dependent Time Series." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86866.

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Processes with long-range correlations or called long-range dependent (LRD) processes are all around us in nature. The presence and nature of LRD is characterized by the Hurst parameter (0 &lt; H &lt; 1). The aim of this paper is to make a practical analysis of the robustness of the Hurst parameter estimators. A simple model of exactly self-similar process-Fractional Gaussian noise (FGN) with parameter H ∈ (0, 1) is applied to evaluate Hurst parameter estimators. The white Gaussian noise or the Symmetric α-stable (SαS) noise is superimposed in order to evaluate the reliability and the robustness of different estimators. In this paper, six statistic analysis methods, R/S statistic, Aggregated Variance method, Absolute Value method, Residuals of Regression method, Periodogram method, and Whittle method are analyzed. It follows from the comparison that the Variance of Residuals method is almost unbiased for non-noise LRD processes. And the Whittle method has best robustness to Symmetric α-stable (SαS) noisy LRD processes. The robustness analysis has practical value for analyzing noisy LRD time series, especially for the economic data, under water signal, biomedical signal and the communication signal which are corrupted by impulsive noise.

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Kharyton, Vsevolod, Grigorios Dimitriadis, and Colin Defise. "A Discussion on the Advancement of Blade Tip Timing Data Processing." In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-63138.

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The Blade Tip Timing method (BTT) is a well-known approach permitting individual blade vibration behavior characterization. The technique is becoming increasingly popular among turbomachinery vibration specialists. Its advantages include its non-intrusive nature and its capability of being used for long-term monitoring, both in on-line and offline analysis. However, the main drawback of BTT is frequency aliasing. Frequency aliasing effects in tip timing can be reduced by means of the application of different methods from digital signal analysis that can exploit the non-uniform nature of the data sampled by BTT. This non-uniformity is due to the fact that an optimization of the circumferential distribution of BTT probes is usually required in order to improve the data quality for targeted modes of blade vibration and/or orders of excitation. The BTT data analysis methods considered in this study are the non-uniform Fourier transform, the minimum variance spectrum estimator approach, a multi-channel technique using in-between samples interpolation, the Lombe-Scargle periodogram and an iterative variable threshold procedure. These methods will be applied to measured data representing quite a large scope of events occurring during gas-turbine compressor operation, e.g. synchronous engine order resonance crossing, rotating stall, suspected limit-cycle oscillations. Finally, the frequency estimates obtained from all these methods will be summarized.

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