Spectral Inference under Complex Temporal Dynamics
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Spectral Inference under Complex Temporal Dynamics. / Yang, Jun; Zhou, Zhou.
In: Journal of the American Statistical Association, Vol. 117, No. 537, 02.01.2022, p. 133-155.Research output: Contribution to journal › Journal article › Research › peer-review
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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