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Generating correlated data for omics simulation. [PDF]
PLoS Comput BiolYang J, Grant GR, Brooks TG.
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A Pseudo-Value Approach to Causal Deep Learning of Semi-Competing Risks. [PDF]
Arab J MathSalerno S, Li Y.
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Distribution functions of multivariate copulas
Statistics & Probability Letters, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rodríguez-Lallena, José A. +1 more
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Copula Functions for Residual Dependency
Psychometrika, 2007Most item response theory models are not robust to violations of conditional independence. However, several modeling approaches (e.g., conditioning on other responses, additional random effects) exist that try to incorporate local item dependencies, but they have some drawbacks such as the nonreproducibility of marginal probabilities and resulting ...
Braeken, Johan +2 more
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Binary copulas as aggregation functions
2014 IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI), 2014We present binary copulas or 2-copulas (copulas, shortly) as important aggregation function. Since generally copulas are non-associative operation we restrict on binary case, which is mostly used in the practice. After giving some introductory part on general aggregation functions we restrict on specific properties of copulas.
Endre Pap, Aniko Szakal
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2017
Copula functions are a group of multivariate distribution functions that join the marginal distribution of multiple variables. They have been used in different fields of science and engineering during the past decades. The main advantage of copulas over other multivariate distribution functions is their flexible structure in choosing marginal ...
Shahrbanou Madadgar, Hamid Moradkhani
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Copula functions are a group of multivariate distribution functions that join the marginal distribution of multiple variables. They have been used in different fields of science and engineering during the past decades. The main advantage of copulas over other multivariate distribution functions is their flexible structure in choosing marginal ...
Shahrbanou Madadgar, Hamid Moradkhani
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2021
This chapter is devoted to a short review of the basic concepts of the theory of dependence functions or copula functions, as they are more usually called. In particular our objective is to define the volume of a copula function: this is one of the basic ingredients of the aggregation algorithm that will be discussed in Part II.
Enrico Bernardi, Silvia Romagnoli
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This chapter is devoted to a short review of the basic concepts of the theory of dependence functions or copula functions, as they are more usually called. In particular our objective is to define the volume of a copula function: this is one of the basic ingredients of the aggregation algorithm that will be discussed in Part II.
Enrico Bernardi, Silvia Romagnoli
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Design hyetograph analysis with 3-copula function
Hydrological Sciences Journal, 2006Abstract A design hyetograph is a synthetic rainfall temporal pattern associated with a return period, usually determined by means of statistical analysis of observed mean rainfall intensity through intensity—duration—frequency (IDF) curves. Since the univariate approach is simple to apply and data availability is scarce, only the mean intensity of a ...
Grimaldi S, Serinaldi F
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d-Dimensional dependence functions and Archimax copulas
Fuzzy Sets and Systems, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mesiar, Radko, Jágr, Vladimír
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