dc.contributor.author Chen, Tianbo dc.contributor.author Sun, Ying dc.contributor.author Li, Ta-Hsin dc.date.accessioned 2020-09-15T12:59:59Z dc.date.available 2019-12-18T12:49:01Z dc.date.available 2020-09-15T12:59:59Z dc.date.issued 2020-08-29 dc.identifier.citation Chen, T., Sun, Y., & Li, T.-H. (2021). A semi-parametric estimation method for the quantile spectrum with an application to earthquake classification using convolutional neural network. Computational Statistics & Data Analysis, 154, 107069. doi:10.1016/j.csda.2020.107069 dc.identifier.doi 10.1016/j.csda.2020.107069 dc.identifier.uri http://hdl.handle.net/10754/660677 dc.description.abstract In this paper, a new estimation method is introduced for the quantile spectrum, which uses a parametric form of the autoregressive (AR) spectrum coupled with nonparametric smoothing. The method begins with quantile periodograms which are constructed by trigonometric quantile regression at different quantile levels, to represent the serial dependence of time series at various quantiles. At each quantile level, we approximate the quantile spectrum by a function in the form of an ordinary AR spectrum. In this model, we first compute what we call the quantile autocovariance function (QACF) by the inverse Fourier transformation of the quantile periodogram at each quantile level. Then, we solve the Yule-Walker equations formed by the QACF to obtain the quantile partial autocorrelation function (QPACF) and the scale parameter. Finally, we smooth QPACF and the scale parameter across the quantile levels using a nonparametric smoother, convert the smoothed QPACF to AR coefficients, and obtain the AR spectral density function. Numerical results show that the proposed method outperforms other conventional smoothing techniques. We take advantage of the two-dimensional property of the estimators and train a convolutional neural network (CNN) to classify smoothed quantile periodogram of earthquake data and achieve a higher accuracy than a similar classifier using ordinary periodograms. dc.description.sponsorship This research was supported by funding from King Abdullah University of Science and Technology (KAUST). We would like to thank the editor, associate editor, and reviewers for their valuable comments. dc.publisher Elsevier BV dc.relation.url https://arxiv.org/pdf/1910.07155 dc.rights Archived with thanks to arXiv dc.title A Semi-Parametric Estimation Method for the Quantile Spectrum with an Application to Earthquake Classification Using Convolutional Neural Network dc.type Preprint dc.contributor.department Statistics Program dc.contributor.department Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division dc.eprint.version Pre-print dc.contributor.institution IBM Watson Research Center, NY, US dc.identifier.arxivid 1910.07155 kaust.person Chen, Tianbo kaust.person Sun, Ying refterms.dateFOA 2019-12-18T12:49:39Z dc.date.published-online 2020-08-29 dc.date.published-print 2021-02 dc.date.posted 2019-10-16
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