G Families of Probability Distributions

G Families of Probability Distributions PDF Author: Mir Masoom Ali
Publisher: CRC Press
ISBN: 1000860353
Category : Mathematics
Languages : en
Pages : 365

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Book Description
Statistical distributions are essential tools to model the characteristics of datasets, such as right or left skewness, bi-modality or multi-modality observed in different applied sciences, such as engineering, medicine, and finance. The well-known distributions like normal, Weibull, gamma and Lindley are extensively used because of their simple forms and identifiability properties. In the last decade, researchers have focused on the more complex and flexible distributions, referred to as Generalized or simply G families of probability distributions, to increase the modelling capability of these distributions by adding one or more shape parameters. The main aim of this edited book is to present new contributions by researchers in the field of G families of probability distributions. The book will help researchers to: Develop new univariate continuous and discrete G families of probability distributions. Develop new bivariate continuous and discrete G families of probability distributions. Derive beneficial mathematical properties such as ordinary and incomplete moments, moment generating functions, residual life and reversed residual life functions, order statistics, quantile spread ordering and entropies, and some bivariate and multivariate extensions of the new and existing models using a simple-type copula.

G Families of Probability Distributions

G Families of Probability Distributions PDF Author: Mir Masoom Ali
Publisher: CRC Press
ISBN: 1000860353
Category : Mathematics
Languages : en
Pages : 365

Get Book Here

Book Description
Statistical distributions are essential tools to model the characteristics of datasets, such as right or left skewness, bi-modality or multi-modality observed in different applied sciences, such as engineering, medicine, and finance. The well-known distributions like normal, Weibull, gamma and Lindley are extensively used because of their simple forms and identifiability properties. In the last decade, researchers have focused on the more complex and flexible distributions, referred to as Generalized or simply G families of probability distributions, to increase the modelling capability of these distributions by adding one or more shape parameters. The main aim of this edited book is to present new contributions by researchers in the field of G families of probability distributions. The book will help researchers to: Develop new univariate continuous and discrete G families of probability distributions. Develop new bivariate continuous and discrete G families of probability distributions. Derive beneficial mathematical properties such as ordinary and incomplete moments, moment generating functions, residual life and reversed residual life functions, order statistics, quantile spread ordering and entropies, and some bivariate and multivariate extensions of the new and existing models using a simple-type copula.

Modeling of Generalized Families of Probability Distributions in the Quantile Statistical Universe

Modeling of Generalized Families of Probability Distributions in the Quantile Statistical Universe PDF Author: Paul J. Van Staden
Publisher:
ISBN:
Category :
Languages : en
Pages : 430

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Book Description


Beyond Beta

Beyond Beta PDF Author: Samuel Kotz
Publisher: World Scientific
ISBN: 9812561153
Category : Mathematics
Languages : en
Pages : 308

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Book Description
Statistical distributions are fundamental to Statistical Science and are a prime indispensable tool for its applications. This monograph is the first to examine an important but somewhat neglected field — univariate continuous distribution on a bounded domain, excluding the beta distribution. It provides an elementary but thorough discussion of “novel” contributions developed in recent years, such as the two-sided power, generalized trapezoidal and generalized Topp and Leone distributions, among others. It discusses a general framework for constructing two-sided distributions and some of its properties. It contains a comprehensive chapter on the triangular distribution as well as a chapter on earlier extensions not emphasized in existing literature. Special attention is given to estimation, in particular, non-standard maximum likelihood procedures. The applications are drawn mainly from the econometric and engineering domains.

Lagrangian Probability Distributions

Lagrangian Probability Distributions PDF Author: Prem C. Consul
Publisher: Springer Science & Business Media
ISBN: 0817644776
Category : Mathematics
Languages : en
Pages : 363

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Book Description
Fills a gap in book literature Examines many new Lagrangian probability distributions and their applications to a variety of different fields Presents background mathematical and statistical formulas for easy reference Detailed bibliography and index Exercises in many chapters May be used as a reference text or in graduate courses and seminars on Distribution Theory and Lagrangian Distributions

Life Distributions

Life Distributions PDF Author: Albert W. Marshall
Publisher: Springer Science & Business Media
ISBN: 0387684778
Category : Technology & Engineering
Languages : en
Pages : 785

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Book Description
This book is devoted to the study of univariate distributions appropriate for the analyses of data known to be nonnegative. The book includes much material from reliability theory in engineering and survival analysis in medicine.

Advances in Probability Distributions with Given Marginals

Advances in Probability Distributions with Given Marginals PDF Author: G. Dall'aglio
Publisher: Springer Science & Business Media
ISBN: 9401134669
Category : Mathematics
Languages : en
Pages : 243

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Book Description
'Et moi - ... - si j'avait su comment en rcvenir. One service mathematics has rendered the je n'y serais point alle.' human race. It has put common sense back Jules Verne where it belongs, on the topmost shelf next to the dusty canistcr labelled 'discarded non sense'. The scries is divergent; therefore we may be Eric T. Bell able to do something with it. O. Heaviside Mathematics is a tool for thought. A highly necessary tool in a world where both feedback and non linearities abound. Similarly, all kinds of parts of mathematics serve as tools for other parts and for other sciences. Applying a simple rewriting rule to the quote on the right above one finds such statements as: 'One service topology has rendered mathematical physics .. .'; 'One service logic has rendered com puter science .. .'; 'One service category theory has rendered mathematics .. .'. All arguably true. And all statements obtainable this way form part of the raison d'etre of this series.

Families of Frequency Distributions

Families of Frequency Distributions PDF Author: J. K. Ord
Publisher:
ISBN:
Category : Distribution (Probability theory).
Languages : en
Pages : 248

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Book Description


The Exponential Family of Probability Distributions Generated by Ơ-finite Measures

The Exponential Family of Probability Distributions Generated by Ơ-finite Measures PDF Author: Michael Spencer Waterman
Publisher:
ISBN:
Category : Distribution (Probability theory)
Languages : en
Pages : 100

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Book Description


Necessary and Sufficient Statistics for a Family of Probability Distributions

Necessary and Sufficient Statistics for a Family of Probability Distributions PDF Author: Evgeniĭ Borisovich Dynkin
Publisher:
ISBN:
Category : Distribution (Probability theory)
Languages : en
Pages : 72

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Book Description


Probability Distributions Used in Reliability Engineering

Probability Distributions Used in Reliability Engineering PDF Author: Andrew N O'Connor
Publisher: RIAC
ISBN: 1933904062
Category : Mathematics
Languages : en
Pages : 220

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Book Description
The book provides details on 22 probability distributions. Each distribution section provides a graphical visualization and formulas for distribution parameters, along with distribution formulas. Common statistics such as moments and percentile formulas are followed by likelihood functions and in many cases the derivation of maximum likelihood estimates. Bayesian non-informative and conjugate priors are provided followed by a discussion on the distribution characteristics and applications in reliability engineering.