Testing Nonlinearity with Rényi and Tsallis Mutual Information with an Application in the EKC Hypothesis

Autor: Elif Tuna, Atıf Evren, Erhan Ustaoğlu, Büşra Şahin, Zehra Zeynep Şahinbaşoğlu
Přispěvatelé: TUNA E., EVREN A. A., USTAOĞLU E., Şahin B., Şahinbaşoğlu Z. Z.
Rok vydání: 2022
Předmět:
Information Security and Reliability
Physics and Astronomy (miscellaneous)
Genel Fizik
Sinyal İşleme
Temel Bilimler (SCI)
Mühendislik
ENGINEERING
General Physics and Astronomy
Astronomi ve Astrofizik
BİLGİSAYAR BİLİMİ
BİLGİ SİSTEMLERİ
Information Systems
Communication and Control Engineering
Tsallis mutual information
COMPUTER SCIENCE
INFORMATION SYSTEMS
ASTRONOMY & ASTROPHYSICS
SPACE SCIENCE
Mathematical Physics
ENGINEERING
ELECTRICAL & ELECTRONIC
Computer Sciences
Elektrik ve Elektronik Mühendisliği
Temel Bilimler
Physics
Bilgi Güvenliği ve Güvenilirliği
FİZİK
MATEMATİK
nonlinearity
Fizik ve Astronomi (çeşitli)
Bilgi sistemi
PHYSICS
MATHEMATICAL
Rényi mutual information
Natural Sciences (SCI)
Physical Sciences
Engineering and Technology
Bilgisayar Bilimi
Bilgi Sistemleri
Haberleşme ve Kontrol Mühendisliği
Natural Sciences
Information Systems
General Physics
Uzay bilimi
Fizik
ASTRONOMİ VE ASTROFİZİK
Bilgisayar Bilimleri
Electrical and Electronic Engineering
Engineering
Computing & Technology (ENG)
EKC hypothesis
Astronomy and Astrophysics
Mühendislik
Bilişim ve Teknoloji (ENG)
COMPUTER SCIENCE
Matematiksel Fizik
Fizik Bilimleri
Signal Processing
MÜHENDİSLİK
ELEKTRİK VE ELEKTRONİK
Mühendislik ve Teknoloji
Zdroj: Entropy; Volume 25; Issue 1; Pages: 79
ISSN: 1099-4300
DOI: 10.3390/e25010079
Popis: © 2022 by the authors.The nature of dependence between random variables has always been the subject of many statistical problems for over a century. Yet today, there is a great deal of research on this topic, especially focusing on the analysis of nonlinearity. Shannon mutual information has been considered to be the most comprehensive measure of dependence for evaluating total dependence, and several methods have been suggested for discerning the linear and nonlinear components of dependence between two variables. We, in this study, propose employing the Rényi and Tsallis mutual information measures for measuring total dependence because of their parametric nature. We first use a residual analysis in order to remove linear dependence between the variables, and then we compare the Rényi and Tsallis mutual information measures of the original data with that the lacking linear component to determine the degree of nonlinearity. A comparison against the values of the Shannon mutual information measure is also provided. Finally, we apply our method to the environmental Kuznets curve (EKC) and demonstrate the validity of the EKC hypothesis for Eastern Asian and Asia-Pacific countries.
Databáze: OpenAIRE
Externí odkaz: