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Fitting Copulas with Maximal Entropy. [PDF]
Bubák M, Navara M.
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Empirical entropy for right censored data
Acta Mathematicae Applicatae Sinica, English Series, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhao, Guo-qing, Liang, Wei, He, Shu-yuan
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Asymptotic results for runs and empirical cumulative entropies
Journal of Statistical Planning and Inference, 2015The paper under review deals with sequences of runs and of empirical cumulative entropies. The authors utilize the definition of runs based on differences and ratios of consecutive order statistics as in [\textit{S. Eryilmaz} and \textit{A. Stepanov}, ``Runs in an ordered sequence of random variables'', Metrika 67, No. 3, 299--311 (2008; Zbl 1357.60037)
Giuliano, R +2 more
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Fast Approximation of Empirical Entropy via Subsampling
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019Empirical entropy refers to the information entropy calculated from the empirical distribution of a dataset. It is a widely used aggregation function for knowledge discovery, as well as the foundation of other aggregation functions such as mutual information.
Chi Wang, Bailu Ding
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and empirical measures, the irregular set and entropy
Ergodic Theory and Dynamical Systems, 2023AbstractFor integers a and $b\geq 2$ , let $T_a$ and $T_b$ be multiplication by a and b on $\mathbb {T}=\mathbb {R}/\mathbb {Z}$ . The action on $\mathbb {T}$ by $T_a$ and $T_b$ is called $\times a,\times b$ action and it is known that, if a and b are multiplicatively independent, then the only $\times a,\times b$ invariant and ergodic ...
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Blockwise empirical entropy tests for time series regressions
Journal of Time Series Analysis, 2005Abstract. This paper shows how the empirical entropy (also known as exponential likelihood or non‐parametric tilting) method can be used to test general parametric hypothesis in time series regressions. To capture the weak dependence of the observations, the paper uses blocking techniques which are also used in the bootstrap literature on time series.
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How changes affect software entropy: an empirical study
Empirical Software Engineering, 2012Context Software systems continuously change for various reasons, such as adding new features, fixing bugs, or refactoring. Changes may either increase the source code complexity and disorganization, or help to reducing it. Aim This paper empirically investigates the relationship of source code complexity and disorganization--measured using source code
Gerardo Canfora +3 more
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Empirical Scale of Side-Chain Conformational Entropy in Protein Folding
Journal of Molecular Biology, 1993A major effect in the energetics of protein folding is the loss of conformational entropy of the side-chains. The definition of entropy as the Boltzmann sampling over all states (S = -R sigma p(i) ln p(i)) requires evaluation of the probability (p(i)) of the system being in rotameric state i. The principle of this paper is to obtain an estimate of p(i)
S D, Pickett, M J, Sternberg
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Emotion recognition using empirical mode decomposition and approximation entropy
Computers & Electrical Engineering, 2018Abstract Automatic human emotion recognition is a key technology for human-machine interaction. In this paper, we propose an electroencephalogram (EEG) feature extraction method that leverages empirical mode decomposition and Approximation Entropy. In our proposed method, Empirical Mode Decomposition (EMD) is used to process EEG signals after data ...
Tian Chen +6 more
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Functional limit theorems and empirical entropy. II
1987This paper is a continuation of the author's article ibid. 33, 35-45 (1986; Zbl 0627.60043); translation from Teor. Veroyatn. Mat. Stat. 33, 31-42 (1985). It contains the second half of the proofs of the theorems presented in part I.
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