Wavelet analysis of covariance with application to atmospheric time series

By Brandon Whitcher, Peter Guttorp, Donald B. Percival. 2000

355 citations recorded by the source corpus.

Subfields: Atmospheric Science, Economics and Econometrics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/ef396559ba0b33879c7d3662d6c54ab901ba80a1

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Citations in the corpus

Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review (2019, methodology): “In finance, the DWT, MODWT and XWT are the principal used types [52,59], where the main advantage is the ability to conserve time information.”
Changes in variance and correlation of soil properties with scale and location: analysis using an adapted maximal overlap discrete wavelet transform (2001, methodology): “Our solutions derive from the developments of wavelet statistics by Percival & Guttorp (1994) (wavelet variance) and by Whitcher et al. (2000) (wavelet covariance and correlation).”
Electrical Power Fluctuations in a Network of DC/AC inverters in a Large PV Plant: relationship between correlation, distance and time scale (2013, background): “The wavelet cross-covariance of two stochastic processes Xt and Yt for scale λj = 2j−1 and lag τ is defined as [24]: γτ,XY(λj) ≡ Cov{W X j,t, W Y j,t+τ} (8)”
Scaling properties of foreign exchange volatility (2001, background): “[22] for a more thorough introduction to the wavelet covariance and its application to observed multivariate time series.”
Age-related changes in modular organization of human brain functional networks (2009, positive): “Frequency-dependent functional connectivity between each pair of regional time series was estimated using wavelet correlations, as previously described (Whitcher et al., 2000; Achard et al., 2006; Bassett et al., 2006; Achard et al., 2008).”
An Intuitive Guide to Wavelets for Economists (2005, methodology): “Gençay, Selçuk, and Whicher (2001) provides two examples of tests for homogeneity of variance - one for an IBM stock price and another for multiple changes in variance using methodology extending this framework which was developed by Whitcher, Guttorp, and Percival (2000a).”
Testing the Conventional and Islamic Financial Market Contagion: Evidence from Wavelet Analysis (2016, methodology): “In a similar way, the wavelet correlation between two time series ρXY (λj) can be represented as the ratio of the wavelet covariance, γXY (λj), and the square root of their wavelet variances σX (λj) and σY (λj) (see Whitcher, Guttorp, and Percival 1999, 2000; Gallegati 2012).”
Maximal Overlap Wavelet Statistical Analysis With Application to Atmospheric Turbulence (2006, methodology): “Whitcher et al. (2000) show that˜ν XY τ j is asymptotically Gaussian distributed, allowing us to construct an approximate confidence interval for the true wavelet covariance via S ˆ ν XY τ j C Φ ¡ 1 1 p ˆ σ XY¨j W M j ¢ ˆ ν XY τ j X Φ ¡ 1 1 p ˆ σ XY¨j W M j T ¢ (22) where Φ ¡ 1 1 p is the p 8 100%…”
Gold, Oil, and Stocks: Dynamic Correlations (2016, neutral): “Following Whitcher, Guttorp, and Percival (2000), we define wavelet correlation ρxy(j) between time series x and y at scale j as: ρxy jð Þ ¼ cov Wx j; sð Þ;Wy j; sð Þ var Wx j; sð Þð Þvar Wy j; sð Þ h i1 2 ≡ γxy jð Þ νx jð Þνy jð Þ ; ð6Þ whereWx(j,s) andWy(j,s) are vectors of theMODWTwavelet…”
Graph analysis of functional brain networks: practical issues in translational neuroscience (2014, methodology): “For example, FC measures based on correlations or partial correlation can be assessed using asymptotic statistics [100, 101].”
A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs (2006, methodology): “are the scale j 2 j 1 MODWT coefficients for {Xt } and {Yt}, respectively, and Nj N Lj 1 (Whitcher et al., 2000).”