Draft:Detrended Partial-Cross-Correlation Analysis
{{Short description|Statistical method for analyzing correlations between non-stationary signals}}
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Detrended Partial-Cross-Correlation Analysis (DPCCA) is a statistical method designed to analyze correlations between two non-stationary signals while accounting for the influence of other variables. This technique builds on the detrended cross-correlation analysis (DCCA){{Cite journal |last1=Podobnik |first1=Boris |last2=Stanley |first2=H. Eugene |date=2008-02-27 |title=Detrended Cross-Correlation Analysis: A New Method for Analyzing Two Nonstationary Time Series |url=https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.100.084102 |journal=Physical Review Letters |volume=100 |issue=8 |pages=084102 |doi=10.1103/PhysRevLett.100.084102|pmid=18352624 |arxiv=0709.0281 |bibcode=2008PhRvL.100h4102P }} by incorporating partial-correlation principles, enabling more accurate quantification of relationships across different time scales{{Cite journal |last1=Yuan |first1=Naiming |last2=Fu |first2=Zuntao |last3=Zhang |first3=Huan |last4=Piao |first4=Lin |last5=Xoplaki |first5=Elena |last6=Luterbacher |first6=Juerg |date=2015-01-30 |title=Detrended Partial-Cross-Correlation Analysis: A New Method for Analyzing Correlations in Complex System |journal=Scientific Reports |language=en |volume=5 |issue=1 |pages=8143 |doi=10.1038/srep08143 |issn=2045-2322 |pmc=4311241 |pmid=25634341|bibcode=2015NatSR...5.8143Y }}.
Overview
DPCCA addresses limitations in traditional methods like filter-based approaches and cross-spectral analysis{{Cite journal |last1=Urban |first1=Frank E. |last2=Cole |first2=Julia E. |last3=Overpeck |first3=Jonathan T. |date=October 2000 |title=Influence of mean climate change on climate variability from a 155-year tropical Pacific coral record |url=https://www.nature.com/articles/35039597 |journal=Nature |language=en |volume=407 |issue=6807 |pages=989–993 |doi=10.1038/35039597 |pmid=11069175 |bibcode=2000Natur.407..989U |issn=1476-4687}}{{Cite journal |last1=Wang |first1=Yongjin |last2=Cheng |first2=Hai |last3=Edwards |first3=R. Lawrence |last4=He |first4=Yaoqi |last5=Kong |first5=Xinggong |last6=An |first6=Zhisheng |last7=Wu |first7=Jiangying |last8=Kelly |first8=Megan J. |last9=Dykoski |first9=Carolyn A. |last10=Li |first10=Xiangdong |date=2005-05-06 |title=The Holocene Asian Monsoon: Links to Solar Changes and North Atlantic Climate |url=https://www.science.org/doi/10.1126/science.1106296 |journal=Science |language=en |volume=308 |issue=5723 |pages=854–857 |doi=10.1126/science.1106296 |pmid=15879216 |bibcode=2005Sci...308..854W |issn=0036-8075}}, which often struggle with subjective parameter selection or assumptions of data stationarity. By removing trends and external influences, DPCCA provides a robust framework for studying complex systems, such as climate dynamics or hydrological processes.
Methodology
The method extends DCCA by integrating partial-correlation techniques, which isolate the direct relationship between two signals while controlling for confounding factors. This allows researchers to identify correlations that are not mediated by other variables, making it particularly useful for non-stationary time series.
Applications
DPCCA has been applied to study phenomena like the interactions between hydrological and climatic variables in the middle-lower reaches of the Yangtze River. In this case, DPCCA demonstrated superior performance compared to DCCA, highlighting its ability to capture nuanced, scale-dependent mechanisms.
Advantages
Key benefits of DPCCA include its flexibility in handling non-stationary data, reduced sensitivity to subjective filter parameters, and enhanced precision in isolating direct correlations. These features make it a valuable tool for analyzing complex systems where multiple variables interact across time scales.
References
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