Definition of "copula"

I begin by arguing in section 2 that there are in fact at least two Celtic copulas, a grammatical copula that simply spells out tense and agreement, and a substantive copula formed on a lexically listed verbal stem.

1994, Randall Hendrick, “8: The Brythonic Celtic copula and head raising”, in David Lightfoot, Norbert Hornstein, editors, Verb Movement, page 163

The theory of conjunctively tensed copulae will be developed and stated with more precision in the following section.

2002, Quentin Smith, Language and Time, page 189

This paper explores the position of the copula in the development of the verb system in second language acquisition of Italian.

2003, Giuliano Bernini, “The copula in learner Italian: Finiteness and verbal inflection”, in Christine Dimroth, Marianne Starren, editors, Information Structure and the Dynamics of Language Acquisition, page 159

The present study focuses on the acquisition of a specific verbal element, namely the copula, in predicative constructions in a cross-linguistic perspective (English, German, Croatian).

2006, Christine Czinglar, Antigone Katiĉić, Katharina Köhler, Chris Schaner-Wolles, edited by Natalia Gagarina and Insa Gülzow, Strategies in the L1-Acquisition of Predication: The Copula Construction in German and Croatian, page 95

In 2000, David X. Li, a banker with a doctorate in statistics who was then at RiskMetrics, part of J. P. Morgan Chase, began using mathematical functions called Gaussian copulas to estimate the likelihood of corporations’ dying in unison.

2009 March 10, Dennis Overbye, “Mathematical Model and the Mortgage Mess”, in New York Times‎

There is little statistical theoretical theory for copulas. Sensitivity studies of estimation procedures and goodness-of-fit tests for copulas are unknown.

2009, N. Balakrishnan, Chin-Diew Lai, Continuous Bivariate Distributions, page 59

Copulas provide an example of the haphazard evolution of quantitative finance. The key result is Sklar's theorem, which says that one can characterize any multivariate probability distribution by its copula (which specifies the correlation structure) and its marginal distributions (the conditional one dimensional distributions). Thus one can create multivariate distributions by mixing and matching copulas and marginal distributions.

2011, Julian Shaw, “Chapter 16: Julian Shaw”, in Richard R. Lindsey, Barry Schachter, editors, How I Became a Quant: Insights from 25 of Wall Street's Elite, page 240

A recently developed flexible method is provided by hierarchical Archimedean copulae (HAC).

2011, Ostap Okhrin, “Chapter 17: Fitting High-Dimensional Copulae to Data”, in Jin-Chuan Duan, Wolfgang Karl Härdle, James E. Gentle, editors, Handbook of Computational Finance, page 482