Spectral Inference under Complex Temporal Dynamics

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Spectral Inference under Complex Temporal Dynamics. / Yang, Jun; Zhou, Zhou.

I: Journal of the American Statistical Association, Bind 117, Nr. 537, 02.01.2022, s. 133-155.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Yang, J & Zhou, Z 2022, 'Spectral Inference under Complex Temporal Dynamics', Journal of the American Statistical Association, bind 117, nr. 537, s. 133-155. https://doi.org/10.1080/01621459.2020.1764365

APA

Yang, J., & Zhou, Z. (2022). Spectral Inference under Complex Temporal Dynamics. Journal of the American Statistical Association, 117(537), 133-155. https://doi.org/10.1080/01621459.2020.1764365

Vancouver

Yang J, Zhou Z. Spectral Inference under Complex Temporal Dynamics. Journal of the American Statistical Association. 2022 jan. 2;117(537):133-155. https://doi.org/10.1080/01621459.2020.1764365

Author

Yang, Jun ; Zhou, Zhou. / Spectral Inference under Complex Temporal Dynamics. I: Journal of the American Statistical Association. 2022 ; Bind 117, Nr. 537. s. 133-155.

Bibtex

@article{b1be8b600cc24ced90fb4eea22b7198d,
title = "Spectral Inference under Complex Temporal Dynamics",
abstract = "We develop a unified theory and methodology for the inference of evolutionary Fourier power spectra for a general class of locally stationary and possibly nonlinear processes. In particular, simultaneous confidence regions (SCR) with asymptotically correct coverage rates are constructed for the evolutionary spectral densities on a nearly optimally dense grid of the joint time-frequency domain. A simulation based bootstrap method is proposed to implement the SCR. The SCR enables researchers and practitioners to visually evaluate the magnitude and pattern of the evolutionary power spectra with asymptotically accurate statistical guarantee. The SCR also serves as a unified tool for a wide range of statistical inference problems in time-frequency analysis ranging from tests for white noise, stationarity and time-frequency separability to the validation for non-stationary linear models.",
author = "Jun Yang and Zhou Zhou",
year = "2022",
month = jan,
day = "2",
doi = "10.1080/01621459.2020.1764365",
language = "English",
volume = "117",
pages = "133--155",
journal = "Journal of the American Statistical Association",
issn = "0162-1459",
publisher = "Taylor & Francis",
number = "537",

}

RIS

TY - JOUR

T1 - Spectral Inference under Complex Temporal Dynamics

AU - Yang, Jun

AU - Zhou, Zhou

PY - 2022/1/2

Y1 - 2022/1/2

N2 - We develop a unified theory and methodology for the inference of evolutionary Fourier power spectra for a general class of locally stationary and possibly nonlinear processes. In particular, simultaneous confidence regions (SCR) with asymptotically correct coverage rates are constructed for the evolutionary spectral densities on a nearly optimally dense grid of the joint time-frequency domain. A simulation based bootstrap method is proposed to implement the SCR. The SCR enables researchers and practitioners to visually evaluate the magnitude and pattern of the evolutionary power spectra with asymptotically accurate statistical guarantee. The SCR also serves as a unified tool for a wide range of statistical inference problems in time-frequency analysis ranging from tests for white noise, stationarity and time-frequency separability to the validation for non-stationary linear models.

AB - We develop a unified theory and methodology for the inference of evolutionary Fourier power spectra for a general class of locally stationary and possibly nonlinear processes. In particular, simultaneous confidence regions (SCR) with asymptotically correct coverage rates are constructed for the evolutionary spectral densities on a nearly optimally dense grid of the joint time-frequency domain. A simulation based bootstrap method is proposed to implement the SCR. The SCR enables researchers and practitioners to visually evaluate the magnitude and pattern of the evolutionary power spectra with asymptotically accurate statistical guarantee. The SCR also serves as a unified tool for a wide range of statistical inference problems in time-frequency analysis ranging from tests for white noise, stationarity and time-frequency separability to the validation for non-stationary linear models.

UR - http://dx.doi.org/10.1080/01621459.2020.1764365

U2 - 10.1080/01621459.2020.1764365

DO - 10.1080/01621459.2020.1764365

M3 - Journal article

VL - 117

SP - 133

EP - 155

JO - Journal of the American Statistical Association

JF - Journal of the American Statistical Association

SN - 0162-1459

IS - 537

ER -

ID: 361385565