MULTIFRACTAL CROSS-CORRELATION ANALYSIS BASED ON STATISTICAL MOMENTS
By Jing Wang, Pengjian Shang, Weijie Ge. 2012
124 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Molecular Biology, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/47e351f7a1f232d7649ae91b2804ebc620f94e7c
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In Economics and Econometrics
- Ladislav Kristoufek
- A. M. S. Filho
- M. F. D. Silva
- Gilney F. Zebende
- Elie Bouri
- Eduard Baumöhl
- Ayoub Ammy-Driss
- Matthieu Garcin
In Molecular Biology
In Statistical and Nonlinear Physics
Citations in the corpus
- Detrending moving-average cross-correlation coefficient: Measuring cross-correlations between non-stationary series (2013, background): “For this purpose, various estimators of power-laws in crosscorrelations of a pair of series have been proposed [13, 19, 8, 6, 15].”
- Mixed-correlated ARFIMA processes for power-law cross-correlations (2013, methodology): “[32]— multifractal cross-correlation analysis based on statisticalmoments (MFSMXA).”
- Measuring correlations between non-stationary series with DCCA coefficient (2013, methodology): “(APE) [23] andmultifractal cross-correlation analysis based on statistical moments (MFSMXA) [24].”
- Autocorrelation and cross-correlation in time series of homicide and attempted homicide (2014, methodology): “We can point out other methods of estimation of the λ [60–63].”