United Statistical Algorithms and Data Science: An Introduction to the Principles

Mukhopadhyay, Subhadeep

doi:10.1007/978-3-030-57306-5_33

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Theory and Approximation Methods2

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2020

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Cited by 1 publication

(2 citation statements)

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“…Note that our custom-constructed basis functions are orthonormal polynomials of mid-rank transform-for more details see Mukhopadhyay (2019), Mukhopadhyay and Wang (2019),…”

Section: Theory and Approximation Methodsmentioning

confidence: 99%

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Nonparametric universal copula modeling

Mukhopadhyay

Parzen

2020

Appl Stoch Models Bus & Ind

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To handle the ubiquitous problem of “dependence learning,” copulas are quickly becoming a pervasive tool across a wide range of data‐driven disciplines encompassing neuroscience, finance, econometrics, genomics, social science, machine learning, healthcare, and many more. At the same time, despite their practical value, the empirical methods of “learning copula from data” have been unsystematic with full of case‐specific recipes. Taking inspiration from modern LP‐nonparametrics, this paper presents a modest contribution to the need for a more unified and structured approach of copula modeling that is simultaneously valid for arbitrary combinations of continuous and discrete variables.

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“…Note that our custom-constructed basis functions are orthonormal polynomials of mid-rank transform-for more details see Mukhopadhyay (2019), Mukhopadhyay and Wang (2019),…”

Section: Theory and Approximation Methodsmentioning

confidence: 99%

“…The LPcomeans are the nonparametrically derived "parameters" of our copula model, which can be interpreted as a dependence measure. For example, many traditional nonparametric Mukhopadhyay (2019). Given bivariate data, compute and display matrix of empirical LP-comeans (see Table 1) LP(j, k; X, Y ) for j, k = 1, .…”

Section: Theory and Approximation Methodsmentioning

confidence: 99%