Time-varying wavelet estimation and deconvolution for nonstationary data based on a FWE function

2020 ◽  
Vol 183 ◽  
pp. 104198
Author(s):  
Yumeng Jiang ◽  
Siyuan Cao ◽  
Siyuan Chen ◽  
Hang Wang ◽  
Hengchang Dai ◽  
...  
Keyword(s):  
Time Varying ◽  
Wavelet Estimation ◽  
Nonstationary Data

Wavelet estimation in time-varying coefficient models

2019 ◽  
Vol 59 (2) ◽  
pp. 276-293
Author(s):  
Xingcai Zhou ◽  
Beibei Ni ◽  
Chunhua Zhu
Keyword(s):  
Varying Coefficient Models ◽  
Time Varying ◽  
Wavelet Estimation ◽  
Varying Coefficient

TIME-VARYING SEISMIC WAVELET ESTIMATION FROM NONSTATIONARY SEISMIC DATA

2017 ◽  
Vol 60 (2) ◽  
pp. 191-202
Author(s):  
FENG Wei ◽  
HU Tian-Yue ◽  
YAO Feng-Chang ◽  
ZHANG Yan ◽  
Cui Yong-Fu ◽  
...  
Keyword(s):  
Seismic Data ◽  
Time Varying ◽  
Wavelet Estimation ◽  
Seismic Wavelet

Transient Characteristics of Simple Systems to Modulated Random Noise

1973 ◽  
Vol 40 (1) ◽  
pp. 73-77 ◽  
Author(s):  
R. L. Barnoski ◽  
J. R. Maurer
Keyword(s):  
Random Noise ◽  
Structural Systems ◽  
Time Varying ◽  
Shaping Filter ◽  
Spectral Estimates ◽  
Nonstationary Response ◽  
Mean Square Response ◽  
Nonstationary Data ◽  
The Mean ◽  
Simple Systems

Discussed are the mean-square response exceedance characteristics of a single-tuned system to amplitude modulated noise. The results bear on the accuracy of spectral estimates of nonstationary data, and subsequently, relate directly to the design, analysis, and testing of structural systems in environments as gusts, earthquakes, and ignition transients. For noise correlated as an exponentially damped cosine, the nonstationary response may exceed its stationary value by a factor in excess of two. A time-varying shaping filter explanation is offered for this behavior. For white noise, such exceedances do not occur.

scholarly journals The evaluation of regional frequency analyses methods for nonstationary data

2015 ◽  
Vol 371 ◽  
pp. 95-98 ◽  
Author(s):  
W. Nam ◽  
S. Kim ◽  
H. Kim ◽  
K. Joo ◽  
J.-H. Heo
Keyword(s):  
Extreme Value Distribution ◽  
Location Parameter ◽  
Generalized Extreme Value Distribution ◽  
Regional Frequency Analysis ◽  
Time Varying ◽  
Shape Parameters ◽  
Index Flood ◽  
Nonstationary Data ◽  
Time Invariant ◽  
Regional Frequency

Abstract. Regional frequency analysis is widely used to estimate more reliable quantiles of extreme hydro-meteorological events. The stationarity of data is required for its application. This assumption tends to be violated due to climate change. In this paper, four nonstationary index flood models were used to analyze the nonstationary regional data. Monte Carlo simulation was used to evaluate the performances of these models for the generalized extreme value distribution with linearly time varying location parameter and constant scale and shape parameters. As a results, it was found that the index flood model with time-invariant index flood and time-variant growth curve could yield more statistically efficient quantile when record is long enough to show significant nonstationarity.

scholarly journals Nonlinear Wavelet Estimation of Time-Varying Autoregressive Processes

Bernoulli ◽  
1999 ◽  
Vol 5 (5) ◽  
pp. 873 ◽  
Author(s):  
Rainer Dahlhaus ◽  
Michael H. Neumann ◽  
Rainer Von Sachs
Keyword(s):  
Time Varying ◽  
Autoregressive Processes ◽  
Wavelet Estimation ◽  
Estimation Of Time
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