On weighted cumulative residual Tsallis entropy and its dynamic version

Khammar, Amir Hamzeh; Jahanshahi, Seyed Mahdi Amir

doi:10.1016/j.physa.2017.09.079

Cited by 21 publications

(10 citation statements)

References 27 publications

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“…51,52 Shannon 53 first proposed the concept of information entropy to describe the uncertainty and has applied in a lots of fields. 54,55 However, as in typical physical problems, there are some examples where the Boltzmann-Shannon entropy is not suitable. 56,57 In 1988, Tsallis proposed a non-extensive entropy called Tsallis entropy.…”

Section: Further Discussionmentioning

confidence: 99%

The generalization negation of probability distribution and its application in target recognition based on sensor fusion

Gao

Deng

2019

International Journal of Distributed Sensor Networks

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Target recognition in uncertain environments is a hot issue. Fusion rules are used to combine the sensor reports from different sources. In this situation, obtaining more information to make correct decision is an essential issue. Probability distribution is one of the most used methods to represent uncertainty information. In addition, the negation of probability distribution provides a new view to represent the uncertainty information. In this article, the existing negation of probability distribution is extended with Tsallis entropy. The main reason is that different systems have different parameter q. Some numerical examples are used to demonstrate the efficiency of the proposed method. Besides, the article also discusses the application of negation in target recognition based on sensor fusion to further demonstrate the importance of negation.

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Section: Further Discussionmentioning

confidence: 99%

The generalization negation of probability distribution and its application in target recognition based on sensor fusion

Gao

Deng

2019

International Journal of Distributed Sensor Networks

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“…However, achieving this desirable behavior using biologically realistic network is a challenge. Analogous to (6.4), Khammar and Jahanshahi [15] have introduced the concepts of weighted cumulative residual Tsallis entropy (WCRTE), and its residual form, defined as…”

Section: Weighted Cumulative Residual Tsallis Entropy)mentioning

confidence: 99%

Some Results on Quantile-based Dynamic Survival and Failure Tsallis Entropy

Kumar¹,

Rani

Singh

2021

Tamkang J. Math.

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Non-additive entropy measures are important for many applications. In this paper, we introduce a quantile-based non-additive entropy measure, based on Tsallis entropy and study their properties. Some relationships of this measure with well-known reliability mea- sures and ageing classes are studied and some characterization results are presented. Also the concept of quantile-based shift independent entropy measures has been introduced and studied various properties.

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“…e study in [10] shows every minute of the six years of entropy-dependent usage data between 1999 and 2004 based on time series and volatility, and that the entropy of the fluctuation series is based on the stock market. Khammar and Jahanshahi [11] submitted the weighted condition of this measure and named it "Weighted Cumulative Residual Tsallis Entropy (WCRTE)" and showed that it can specify the value of the survival function and Rayleigh distribution in a unique way. In 2019, Karakas [12] has attained volatility of ethereum and bitcoin, and then, the same author [13] used the world indices such as Istanbul Stock Indices (BIST 30), Brazil Index (Bovespa), Germany Index (DAX 30), Britain Index (FTSE100), South Korea (KOSPİ), Japan Index (NIKKEI 225), United States Index (S&P 500), and China Index (SHANGAI) that have been examined over 8 years between 2010 and 2018, and, as a result, found the entropy notion for volatility measure to draw a comparison.…”

Section: Introductionmentioning

confidence: 99%

The Impact of the SARS-CoV-2 Epidemic on World Indices: The Entropy Approach

Karakaş

Doğan

Çalik

2021

Mathematical Problems in Engineering

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The coronavirus disease (COVID-19) outbreak started in December 2019 in Wuhan. The virus has spread around the whole world, and it has caused a strong and serious pandemic. Symptoms such as cough, respiratory distress, diarrhea, and fatigue associated with COVID-19 are typical clinical findings. Coronavirus infection has become an important public health concern because of its increasing prevalence, serious complications, and mortality. In light of this information, we examine different entropy methods for world indices (ISE 30, FTSE 100, NIKKEI 225, SP 500, and DAX 30) in the pre-COVID-19 period (02.01.2019–17.11.2019) and the post-COVID-19 period (18.11.2019–23.11.2020) in this article. Besides, we discuss the performances of entropies such as Shannon, Renyi, Tsallis, and approximate entropy (ApEn) in detail and perform the notion of entropy for volatility measure. As a result, we present the numerical results for the data set.

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On weighted cumulative residual Tsallis entropy and its dynamic version

Cited by 21 publications

References 27 publications

The generalization negation of probability distribution and its application in target recognition based on sensor fusion

The generalization negation of probability distribution and its application in target recognition based on sensor fusion

Some Results on Quantile-based Dynamic Survival and Failure Tsallis Entropy

The Impact of the SARS-CoV-2 Epidemic on World Indices: The Entropy Approach

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