Results 21 to 30 of about 1,503,812 (288)
Measures of Dispersion and Serial Dependence in Categorical Time Series
Econometrics, 2019 The analysis and modeling of categorical time series requires quantifying the extent of dispersion and serial dependence. The dispersion of categorical data is commonly measured by Gini index or entropy, but also the recently proposed extropy measure can
Christian H. Weiß
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On New Three- and Two-Dimensional Ratio-Power Copulas
Computational Journal of Mathematical and Statistical Sciences, 2023 In recent decades, a great variety of dependence models for data analysis have been elaborated. Among them, those based on copulas have demonstrated a great ability to capture the possible dependence between quantitative measures.
Christophe Chesneau
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Measurement dependent locality
New Journal of Physics, 2016 New Journal of Physics ...
Pütz, Gilles, Gisin, Nicolas
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Survival Copula Entropy and Dependence in Bivariate Distributions
Revstat Statistical JournalIn the present work we propose survival copula entropy as an alternative to Shannon entropy, cumulative residual entropy and copula entropy measures in computing the uncertainty in bivariate populations.
S.M. Sunoj , N. Unnikrishnan Nair
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Entropy, 2022 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
Elif Tuna +4 more
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Efficient Modeling of the Energy Sector Using a New Bivariate Copula
MathematicsCopulas are a useful tool to generate bivariate distributions from the univariate marginals. This method is also useful to generate bivariate families of distributions. In this paper, a new copula has been proposed. Some useful properties of the proposed
Jumanah Ahmed Darwish +1 more
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Domination of sample maxima and related extremal dependence measures
Dependence Modeling, 2018 For a given d-dimensional distribution function (df) H we introduce the class of dependence measures μ(H, Q) = −E{n H(Z1, . . . , Zd)}, where the random vector (Z1, . . . , Zd) has df Q which has the same marginal dfs as H. If both H and Q are max-stable
Hashorva Enkelejd
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On Quantifying Dependence: A Framework for Developing Interpretable Measures
, 2013 We present a framework for selecting and developing measures of dependence when the goal is the quantification of a relationship between two variables, not simply the establishment of its existence.
Nicolae, Dan L., Reimherr, Matthew
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Multidimensional dependency measures
Journal of Multivariate Analysis, 2004 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fernández, Begoña Fernández +1 more
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On the Sample Information About Parameter and Prediction [PDF]
, 2010 The Bayesian measure of sample information about the parameter, known as Lindley's measure, is widely used in various problems such as developing prior distributions, models for the likelihood functions and optimal designs.
Ehsan S. Soofi +6 more
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