Dependence Modeling

ISSN: 2300-2298

First published: October 21, 2013

Edited by: Giovanni Puccetti

About this journal

Special Issue:

Special Issue in Memory of Abe Sklar

The journal Dependence Modeling aims at providing a medium for exchanging results and ideas in the area of multivariate dependence modeling.
It is an open access fully peer-reviewed journal providing the readers with free, instant, and permanent access to all content worldwide.
Dependence Modeling is listed by Web of Science (Emerging Sources Citation Index), Scopus, MathSciNet and Zentralblatt Math.

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The journal presents different types of articles:

"Research Articles" on fundamental theoretical aspects, as well as on significant applications in science, engineering, economics, finance, insurance and other fields.
"Review Articles" which present the existing literature on the specific topic from new perspectives.
"Interview articles" limited to two papers per year, covering interviews with milestone personalities in the field of Dependence Modeling.

The journal topics include (but are not limited to):　

Copula methods
Multivariate distributions
Estimation and goodness-of-fit tests
Measures of association
Quantitative risk management
Risk measures and stochastic orders
Time series
Environmental sciences
Computational methods and software
Extreme-value theory
Limit laws
Mass Transportations

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first journal focusing on the modeling of dependence
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Supplementary Materials

Topics

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Volume 9 Issue 1

January 2021

Generalized Bernoulli process with long-range dependence and fractional binomial distribution

Jeonghwa Lee March 1, 2021

More Cite
Open Access PDF PDF

Abstract

Bernoulli process is a finite or infinite sequence of independent binary variables, X i , i = 1, 2, · · ·, whose outcome is either 1 or 0 with probability P ( X i = 1) = p , P ( X i = 0) = 1 – p , for a fixed constant p ∈ (0, 1). We will relax the independence condition of Bernoulli variables, and develop a generalized Bernoulli process that is stationary and has auto-covariance function that obeys power law with exponent 2 H – 2, H ∈ (0, 1). Generalized Bernoulli process encompasses various forms of binary sequence from an independent binary sequence to a binary sequence that has long-range dependence. Fractional binomial random variable is defined as the sum of n consecutive variables in a generalized Bernoulli process, of particular interest is when its variance is proportional to n 2 H , if H ∈ (1/2, 1).

Cite Score	1.5
Mathematical Citation Quotient	0.21
SCImago Journal Rank	0.752
Source Normalized Impact per Paper	0.993

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Reviewers are asked to provide detailed, constructive comments that will help both the editors make a decision on the publication and the author(s) to improve their manuscript. They should point out whether the work has serious flaws that preclude its publication, or whether additional experiments should be carried out or additional data should be collected to support the conclusions drawn.

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Editorial

Editor-in-Chief
Giovanni Puccetti, Università degli Studi di Milano, Italy　

Associate Editors
Fabrizio Durante, Università del Salento, Italy
Matthias Scherer, Technische Universität München, Germany
Steven Vanduffel, Vrije Universiteit Brussel, Belgium

Honorary Board Members:
Roger Cooke, Resources for the Future, USA
Claudia Czado, Technische Universität München, Germany
Paul Embrechts, ETH Zürich, Switzerland
Christian Genest, McGill University, Canada
Harry Joe, University of British Columbia, Canada
Roger Nelsen, Lewis & Clark College, USA
Ludger Rüschendorf, Albert-Ludwigs-Universität Freiburg, Germany

Editorial Advisory Board Members:
Beatrice Acciaio, The London School of Economics and Political Science, UK
Carole Bernard, Vrije Universiteit Brussel, Belgium & Grenoble EM, France
Corina Constantinescu, University of Liverpool, UK
Elena Di Bernardino, Conservatoire national des arts et métiers (CNAM, Paris), France
Sebastian Engelke, Université de Genève, Switzerland
Jean-David Fermanian, ENSAE Paris, France
Juan Fernández-Sánchez, Universidad de Almería, Spain
Stéphane Girard, Inria Grenoble Rhône-Alpes, France
Ingrid Hobæk Haff, University of Oslo, Norway
Enkelejd Hashorva, Université de Lausanne, Switzerland
Taizhong Hu, University of Science and Technology of China
Lei Hua, Northern Illinois University, USA
Piotr Jaworski, Uniwersytet Warszawski, Poland
Dorota Kurowicka, TU Delft, Netherlands
Woojoo Lee, Seoul National University, Republic of Korea
Giampiero Marra, University College London, UK
Ostap Okhrin, Technische Universität Dresden, Germany
Artem Prokhorov, The University of Sydney Business School, Australia
Jean-Francois Quessy, Université du Québec à Trois-Rivières, Canada
Peng Shi, University of Wisconsin-Madison, USA
Wolfgang Trutschnig, Universität Salzburg, Austria
Emiliano Valdez, University of Connecticut, USA
Ruodu Wang, University of Waterloo, Canada

Assistant Editors
Andrew Chernih, University of New South Wales, Australia
Sebastian Fuchs, Universität Salzburg, Austria
Marta Nai Ruscone, Università degli Studi di Genova, Italy

Publisher
DE GRUYTER Poland
Bogumiła Zuga 32A Str.
01-811 Warsaw, Poland
T: +48 22 701 50 15

Editorial Contact
Giovanni Puccetti
demo.editorial@degruyter.com

Dependence Modeling

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