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Hierarchical Archimedean Copulas [electronic resource] / by Jan Górecki, Ostap Okhrin.

By:

Górecki, Jan [author.]

Contributor(s):

Material type:

TextSeries: SpringerBriefs in Applied Statistics and EconometricsPublisher: Cham : Springer Nature Switzerland : Imprint: Springer, 2024Edition: 1st ed. 2024Description: XII, 120 p. 28 illus., 7 illus. in color. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783031563379

Subject(s):

-- Computer programs

Additional physical formats: Printed edition:: No title; Printed edition:: No titleLOC classification:

QA276-280

Online resources:

Resursa online

Contents:

Preface -- 1 Copulas -- 2 Archimedean Copulas -- 3 Construction -- 4 Properties -- 5 Sampling -- 6 Estimation -- 7 Temporal Models and their Applications -- 8 Software.

In: Springer Nature eBookSummary: This book offers a thorough understanding of Hierarchical Archimedean Copulas (HACs) and their practical applications. It covers the basics of copulas, explores the Archimedean family, and delves into the specifics of HACs, including their fundamental properties. The text also addresses sampling algorithms, HAC parameter estimation, and structure, and highlights temporal models with applications in finance and economics. The final chapter introduces R, MATLAB, and Octave toolboxes for copula modeling, enabling students, researchers, data scientists, and practitioners to model complex dependence structures and make well-informed decisions across various domains.

Item type: List(s) this item appears in: Titluri cărți electronice Springer Nature 2025 (10631 titluri)

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Preface -- 1 Copulas -- 2 Archimedean Copulas -- 3 Construction -- 4 Properties -- 5 Sampling -- 6 Estimation -- 7 Temporal Models and their Applications -- 8 Software.

This book offers a thorough understanding of Hierarchical Archimedean Copulas (HACs) and their practical applications. It covers the basics of copulas, explores the Archimedean family, and delves into the specifics of HACs, including their fundamental properties. The text also addresses sampling algorithms, HAC parameter estimation, and structure, and highlights temporal models with applications in finance and economics. The final chapter introduces R, MATLAB, and Octave toolboxes for copula modeling, enabling students, researchers, data scientists, and practitioners to model complex dependence structures and make well-informed decisions across various domains.

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