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		<doi>10.1103/physreve.95.052206</doi>
		<issn>1539-3755</issn>
		<label>lattes: 0793627832164040 5 RamosBuPoGoMaKuMa:2017:ReMeCo</label>
		<citationkey>RamosBuPoGoMaKuMa:2017:ReMeCo</citationkey>
		<title>Recurrence measure of conditional dependence and applications</title>
		<year>2017</year>
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		<author>Ramos, Antônio Mário de Torres,</author>
		<author>Builes-Jaramillo, Alejandro,</author>
		<author>Poveda, Germán,</author>
		<author>Goswami, Bedartha,</author>
		<author>Macau, Elbert Einstein Nehrer,</author>
		<author>Kurths, Jürgen,</author>
		<author>Marwan, Norbert,</author>
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		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Universidad Nacional de Colombia</affiliation>
		<affiliation>Universidad Nacional de Colombia</affiliation>
		<affiliation>Potsdam Institute for Climate Impact Research</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Potsdam Institute for Climate Impact Research</affiliation>
		<affiliation>Potsdam Institute for Climate Impact Research</affiliation>
		<electronicmailaddress>antonio.ramos@inpe.br</electronicmailaddress>
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		<electronicmailaddress></electronicmailaddress>
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		<electronicmailaddress>elbert.macau@inpe.br</electronicmailaddress>
		<journal>Physical Review E</journal>
		<volume>95</volume>
		<number>5</number>
		<pages>052206</pages>
		<secondarymark>A1_INTERDISCIPLINAR A1_ENGENHARIAS_III A2_SAÚDE_COLETIVA A2_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA A2_GEOCIÊNCIAS A2_ENGENHARIAS_IV A2_ENGENHARIAS_II A2_CIÊNCIAS_AMBIENTAIS A2_CIÊNCIAS_AGRÁRIAS_I A2_BIODIVERSIDADE A2_ASTRONOMIA_/_FÍSICA B1_QUÍMICA B1_MEDICINA_II B1_MEDICINA_I B1_MATERIAIS B1_FARMÁCIA B1_ECONOMIA B1_CIÊNCIAS_BIOLÓGICAS_I B1_CIÊNCIA_DA_COMPUTAÇÃO B2_CIÊNCIAS_BIOLÓGICAS_II</secondarymark>
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		<keywords>Dinâmica Não Linear, Análise de séries temporais, Causalidade, Determinação de Interações, Gráfico de Recorrência.</keywords>
		<abstract>Identifying causal relations from observational data sets has posed great challenges in data-driven causality inference studies. One of the successful approaches to detect direct coupling in the information theory framework is transfer entropy. However, the core of entropy-based tools lies on the probability estimation of the underlying variables. Herewe propose a data-driven approach for causality inference that incorporates recurrence plot features into the framework of information theory. We define it as the recurrence measure of conditional dependence (RMCD), and we present some applications. The RMCD quantifies the causal dependence between two processes based on joint recurrence patterns between the past of the possible driver and present of the potentially driven, excepting the contribution of the contemporaneous past of the driven variable. Finally, it can unveil the time scale of the influence of the sea-surface temperature of the Pacific Ocean on the precipitation in the Amazonia during recent major droughts.</abstract>
		<area>COMP</area>
		<language>en</language>
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		<notes>Setores de Atividade: Pesquisa e desenvolvimento científico.</notes>
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