3 edition of **Generalized gamma convolutions and related classes of distributions and densities** found in the catalog.

Generalized gamma convolutions and related classes of distributions and densities

Lennart Bondesson

- 226 Want to read
- 12 Currently reading

Published
**1992** by Springer-Verlag in New York .

Written in English

- Convolutions (Mathematics),
- Distribution (Probability theory)

**Edition Notes**

Includes bibliographical references (p. [161]-167) and indexes.

Statement | Lennart Bondesson. |

Series | Lecture notes in statistics ;, 76, Lecture notes in statistics (Springer-Verlag) ;, v. 76. |

Classifications | |
---|---|

LC Classifications | QA273.6 .B66 1992 |

The Physical Object | |

Pagination | viii, 173 p. : |

Number of Pages | 173 |

ID Numbers | |

Open Library | OL1714050M |

ISBN 10 | 0387978666, 3540978666 |

LC Control Number | 92016277 |

References Bondesson, Bondesson, L., Generalized Gamma Convolutions and Related Classes of Distributions and Densities. Springer, New by: It follows that bilateral Gamma distributions are leptokurtic. 3. Related classes of distributions As apparent from the L evy measure (), bilateral Gamma distributions are special cases of generalized tempered stable distributions [5, Chap. ]. This six-parameter family is de ned by its L evy measure F(dx) = + x1+ + e +x1 (0;1)(x) + jxj1.

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About this book. Generalized Gamma convolutions were introduced by Olof Thorin in and were used by him to show that, in particular, the Lognormal distribution is infinitely divisible.

After that a large number of papers rapidly appeared with new results in a somewhat random : Springer-Verlag New York. Generalized Gamma convolutions were introduced by Olof Thorin in and were used by him to show that, in particular, the Lognormal distribution is infinitely divisible. After that a large number of papers rapidly appeared with new results in a somewhat random order.

Many of the papers appeared in the Scandinavian Actuarial by: About this book. Introduction. Generalized Gamma convolutions were introduced by Olof Thorin in and were used by him to show that, in particular, the Lognormal distribution is infinitely divisible.

After that a large number of papers rapidly appeared with new results in a somewhat random order. Generalized Gamma Convolutions and Related Classes of Distributions and Densities Lennart Bondesson (auth.) Generalized Gamma convolutions were introduced by Olof Thorin in and were used by him to show that, in particular, the Lognormal distribution is infinitely divisible.

Open Library is an open, editable library catalog, building towards a web page for every book ever published. Generalized gamma convolutions and related classes of distributions and densities by Lennart Bondesson,Springer edition, paperback.

Generalized Gamma Convolutions and Related Classes of Distributions and Densities Average rating: 0 out of 5 stars, based on 0 reviews Write a review Asbury Harpending. The aim of this monograph is to provide a systematic account of the theory of generalized Gamma convolutions and related classes of probability distributions and densities.

Several well-known probability distributions are treated in the accompanying examples. Generalized Gamma Convolutions and Related Classes of Distributions and Densities. [Lennart Bondesson] -- Generalized Gamma convolutions were introduced by Olof Thorin in and were used by him to show that, in particular, the Lognormal distribution is infinitely divisible.

and Cis a normalizing constant, are generalized gamma convolutions; consequently. all densities (distributions) which are weak limits of densities of the form () are generalized gamma convolutions (and thus inﬁnitely divisible) as : G.G.

Hamedani. This chapter and Chapters 4–7, which treat generalized Gamma convolutions (GGC), form the central body of this work. The class of GGC’s was introduced by 0.

Thorin in as a useful tool for proving ID of particular distributions. This class of distributions, in honor of Thorin often called the ℐ-class, also has its own by: Bondesson L.

() Densities of Generalized Gamma Convolutions. In: Generalized Gamma Convolutions and Related Classes of Distributions and Densities. Lecture Notes in Statistics, vol Cited by: 1. Generalized gamma convolutions and related classes of distributions and densities.

Lecture Notes in Statistics, Springer-Verlag, New York. Mathematical Reviews (MathSciNet): MR [6] Bondesson, L. Classes of infinitely divisible distributions and densities. Wahrsch. According to the central limit theorem, the N(0,1)-distribution with mgf exps 2 /2× is a simple example of such a distribution.

An extension of ℐ to a class of distributions on the whole real line ℝ is thus desirable. This chapter is devoted to the class ℐ e of extended generalized Gamma convolutions (EGGC) introduced by Thorin (a).Cited by: 1. Abstract Let k > 0 be an integer and Y a standard Gamma(k) distributed random variable.

Let X be an independent positive random variable with a density that is hyperboli- cally monotone (HM) of order k: Then Y X and Y=X both have distributions that are generalized gamma convolutions (GGCs).

jk s less than or equal to 1. and C isanormalizingconstant,aregeneralizedgammaconvolutions;consequently. all densities (distributions) which are weak limits of densities of the form () are generalized gamma convolutions (and thus inﬁnitely divisible) as well.

Generalized gamma convolutions and related classes of distributions and densities. Lecture Notes in Statist. 76, Springer, New York, Zentralblatt MATH: T. Cabanal-Duvillard, A matrix representation of the Bercovici-Pata bijection Electron. Probab.

10 (), –Cited by: Generalized Gamma Convolutions and Related Classes of Distributions and Densities. Lecture Notes in Statistics, vol. 76, Springer, New York () Bondesson, Cited by: The generalized Gamma convolutions are characterized among the in nitely divisible distributions by the following property of the corresponding Bernstein function f, namely by f0being a Stieltjes transform, i.e.

of the form f0(s) = a+ Z 1 0 d (x) s+ x; s>0; where a 0 and is a non-negative measure on [0;1[. The relation between and is that d dx. Generalized Gamma Convolutions and Related Classes of Distributions and Densities. Lecture Notes in Statistics New York: Springer.

Mathematical Reviews (MathSciNet): MR Zentralblatt MATH: [5] Erickson, K.B. and Maller, R.A. Generalised Ornstein–Uhlenbeck processes and the convergence of Lévy by: 6. Let k>0 be an integer and Y a standard Gamma(k) distributed random variable.

Let X be an independent positive random variable with a density that is hyperboli-cally monotone (HM) of order YXand Y/Xboth have distributions that are generalized gamma convolutions (GGCs). This result extends a result of Roynette et.

Generalized Gamma Convolutions and Related Classes of Distributions and Densities. Lecture Notes in Statistics New York: Springer.

Mathematical Reviews (MathSciNet): MR [7] Bondesson, L. On hyperbolically monotone by: 2. The analogue of the Gamma distribution is the Negative Binomial distribution.

By a generalized Negative Binomial convolution (GNBC), we mean a limit distribution for a sequence of finite convolutions of Negative Binomial distributions.

The class of GNBC’s is denoted ℐ d. Many of the results valid for ℐ have their counterparts for ℐ d, Author: Lennart Bondesson. ON THE CONVOLUTION OF GAMMA DISTRIBUTIONS BY MOHAMED AKKOUCHI Abstract. In this paper, we give a formula for the distribution of the sum of n independent random variables with gamma distributions.

A formula for such a sum was provided by Mathai (see [5]) in But it was complicated. In the paper [1],File Size: 83KB. The class of distributions on $\mathbb{R}$ generated by convolutions of Γ-distributions and the class generated by convolutions of mixtures of exponential distributions are generalized to higher.

Generalized Gamma Convolutions and Related Classes of Distributions and Densities. Lecture Notes in Statistics New York: Springer.

Mathematical Reviews (MathSciNet): MR [4] Bondesson, L. A problem concerning stable distributions. Technical report, Uppsala by: This paper has two parts.

In the first part some results for generalized gamma convolutions (GGCs) are reviewed. A GGC is a limit distribution for sums of independent gamma : Lennart Bondesson.

Generalized Gamma Convolutions and Related Classes of Distributions and Densities (Lecture Notes Statist.

76). Springer, New York. [10] Brockwell, P. and Davis, R. The class of type G distributions on R d and related subclasses of infinitely divisible distributions. A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text.

Distributions of exponential integrals of independent increment processes related to generalized gamma convolutions Article in Bernoulli 18(4) November with 23 Reads How we measure 'reads'. The generalized gamma distribution is a continuous probability distribution with three parameters.

It is a generalization of the two-parameter gamma distribution. Since many distributions commonly used for parametric models in survival analysis (such as the Exponential distribution, Mean: a, Γ, (, (, d, +, 1,), /, p,), Γ, (, d, /, p,), {\displaystyle a{\frac {\Gamma ((d+1)/p)}{\Gamma (d/p)}}}.

Distributions of exponential integrals of independent increment processes related to generalized gamma convolutions ANITA BEHME1, MAKOTO MAEJIMA2, MUNEYA MATSUI3 and NORIYOSHI SAKUMA4 1TU Braunschweig, Pockelsstr. 14, Braunschweig, Germany.

E-mail: @ 2Keio University,Hiyoshi, Kohoku-ku, YokohamaJapan. VMd = Variance Matrix Gamma (nitely supported Thorin measures). I points in the direction of generalisation; indicates inclusion in spe-cial cases.

The Thorin [58, 59] generalized Gamma convolutions provide a very natural class of distributions on which to base our multivariate Size: 1MB. It follows that bilateral Gamma distributions are leptokurtic. Related classes of distributions As apparent from the Levy measure (), bilateral Gamma distributions are special cases of generalized tempered stable distributions [5, Chap.

This six-parameter family is deﬂned by its Levy measure F(dx) = µ ﬁ+ x1+ﬂ+ e¡‚+x1. Generalized gamma convolutions and related classes of distributions and densities. Springer mixture convolution exponential-distribution infinite-mixture-model.

Generalized Gamma Distribution A general probability form that reduces to many common distributions. There are two shape parameters \(a>0\) and \(c\neq0\). Generalized Gamma Convolutions and Related Classes of Distributions and Densities, Lecture Notes in Statistics (Springer), vol.

76, Springer-Verlag, New York () Google Scholar. Dobrushin and Major, R.L. Dobrushin, P. MajorNon-central limit theorem for Cited by: inﬁnitely divisible laws on R+ called the generalized gamma convolutions (GGC), a class introduced by O.

Thorin in see [49] and then studied thoroughly by L. Bondesson [5]; both the lectures notes by Bondesson and the book by Steutel and Van Harn [48] contain many results on this class of laws. WeCited by: Convolution equivalence and infinite divisibility - Volume 41 Issue 2 - Anthony G.

Pakes. Generalized Gamma Convolutions and Related Classes of Distributions. Springer, New York. Breiman, L. The class of subexponential distributions.

by: Multivariate subordination, self-decomposability and stability - Volume 33 Issue 1 - Ole E. Barndorff-Nielsen, Jan Pedersen, Ken-Iti Sato Generalized Gamma Convolutions and Related Classes of Distributions and Densities (Lecture Notes Statist.

76). Springer, by: Generalized Gamma Convolutions and Related Classes of Distributions and Densities (Lecture Notes Statist. 76). Springer, New York. Durrett, R. and Liggett, T.

().Cited by:. Convolution mixtures of infinitely divisible distributions - Volume 90 Issue 1 - John T. Kent L. A remarkable property of generalized gamma convolutions. Probability Theory and Related Fields, Vol. 78, Issue. 3, p. L. Classes of infinitely divisible distributions and densities.

by: 7.Generalized Gamma Convolutions and Related Classes of Distributions and Densities, Lecture Notes in Statistics, vol. 76, Springer-Verlag, New York ()Cited by: 2.The term is motivated by the fact that the probability mass function or probability density function of a sum of independent random variables is the convolution of their corresponding probability mass functions or probability density functions respectively.

Many well known distributions have simple convolutions. The following is a list of these.