On the dynamic cumulative residual entropy

Asadi, Majid; Zohrevand, Younes

doi:10.1016/j.jspi.2006.06.035

Cited by 201 publications

(107 citation statements)

References 8 publications

Supporting

Mentioning

106

Contrasting

Order By: Relevance

“…This result was proved for n = k = 1 in Theorem 4.8 of Asadi and Zohrevand(2007). Now, we assume that the result is true for n − 1 and we are going to prove it for n. We are assuming that for c > 0, E n,k (X;t) = ckE n−1,k (X;t)…”

Section: Some Properties and Characterization Resultsmentioning

confidence: 93%

An Extension of Generalized Cumulative Residual Entropy

Tahmasebi¹,

Eskandarzadeh²,

Jafari³

2017

JSTA

View full text Add to dashboard Cite

In this paper, a new extension of cumulative residual entropy is proposed. It contain the generalized cumulative residual entropy introduced by Psarrakos and Navarro (2013) and is related with the k-record values. We also consider a dynamic version of this new cumulative residual entropy using the residual lifetime. For these concepts, we obtain some properties similar to generalized cumulative residual entropy in stochastic ordering and aging classes properties.

show abstract

Section: Some Properties and Characterization Resultsmentioning

confidence: 93%

An Extension of Generalized Cumulative Residual Entropy

Tahmasebi¹,

Eskandarzadeh²,

Jafari³

2017

JSTA

View full text Add to dashboard Cite

show abstract

“…Large amount of following studies have successively introduced the concepts of the expansion of Hartley entropy and Shannon entropy [16], relative entropy [17], cumulative residual entropy [18][19][20][21], joint entropy [22,23], conditional entropy [24][25][26], mutual information [27][28][29][30][31][32], cross entropy [33][34][35][36][37][38], fuzzy entropy [15,39], maximum entropy principle [40,41] and minimum cross-entropy principle [42,43], and a series of achievements have been made in these aspects. Zhong makes use of general information functions to unify the methods of describing information metrics with Entropy formulas [4].…”

Section: About the Metrics Of Informationmentioning

confidence: 99%

Objective information theory: A Sextuple model and 9 kinds of metrics

Shen

et al. 2014

2014 Science and Information Conference

View full text Add to dashboard Cite

In the contemporary era, the importance of information is undisputed, but there has never been a common understanding of information, nor a unanimous conclusion to the researches on information metrics. Based on the previous studies, this paper analyzes the important achievements in the researches of the properties and metrics of information as well as their main insufficiencies, and discusses the essence and connotation, the mathematical expressions and other basic problems related to information. On the basis of the understanding of the objectivity of information, it proposes the definitions and a Sextuple model of objective information; discusses the basic properties of information, and brings forward the definitions and mathematical expressions of nine kinds of metrics of information, i.e., extensity, detailedness, sustainability, richness, containability, delay, distribution, validity and matchability. Through these, this paper establishes a basic theory frame of Objective Information Theory to support the analysis and research on information and information system systematically and comprehensively.

show abstract

“…Asadi et al (2005), Abraham and Sankaran (2005) studied the dynamic Renyi entropy of order α and its properties. Asadi and Zohrevand (2007) proposed the dynamic cumulative residual entropy. Abbasnejad et al (2010) introduced the dynamic survival entropy of order α.…”

Section: Theorem 3 Under the Assumptions Of Theorem 1 F And G Belonmentioning

confidence: 99%

“…For more properties of CRE, one can refer to Wang and Vemuri (2007) and Rao (2005). Asadi and Zohrevand (2007) proposed a dynamic form of CRE. Zografos and Nadarajah (2005) introduced the survival entropy(SE) of order α as…”

Section: Introductionmentioning

confidence: 99%

Some Characterization Results Based on Dynamic Survival and Failure Entropies

Abbasnejad¹

2011

Communications for Statistical Applications and Methods

View full text Add to dashboard Cite

In this paper, we develop some characterization results in terms of survival entropy of the first order statistic. In addition, we generalize the cumulative entropy recently proposed by Di Crescenzo and Logobardi (2009) to a new measure of information (called the failure entropy) and study some properties of it and its dynamic version. Furthermore, power distribution is characterized based on dynamic failure entropy.

show abstract

On the dynamic cumulative residual entropy

Cited by 201 publications

References 8 publications

An Extension of Generalized Cumulative Residual Entropy

An Extension of Generalized Cumulative Residual Entropy

Objective information theory: A Sextuple model and 9 kinds of metrics

Some Characterization Results Based on Dynamic Survival and Failure Entropies

Contact Info

Product

Resources

About