G Families of Probability Distributions

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advanced probability theory
Ali Mikhail Haq Copula
Bathtub Shaped Hazard Functions
Carbon Fibres Data
Category=PBT
Category=PS
Category=UB
Category=UY
CDF
Clayton copula
Conditional Expectation
continuous distribution modeling techniques
copula theory applications
Cramer-von-Mises Estimation
Data Sets
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eq_computing
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eq_isMigrated=2
eq_nobargain
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Farlie-Gumbel-Morgenstern
Fisher's Information Matrix
Fisher’s Information Matrix
Generalized Exponential Distribution
Generating Function
Glass Fibres Data
Hazard Rate Function
Hazard Rate Order
Increasing Failure Rate Function
KS Test Statistic
Kumaraswamy Distribution
L-Moments
Length Biased Distributions
lifetime data analysis
Lindley Distribution
Maximum Likelihood Estimation
Moments
Monthly Rainfall Data
Multicomponent Stress Strength
multivariate distribution models
Order Statistics
Poisson G Family
Regression Model
reliability engineering statistics
Residual Life Functions
Residual Life Order
Reverse Hazard Function
Reversed Hazard Rate Function
statistical modeling methods
Stress Strength Reliability
Topp Leone Family
TTT Plot

Product details

  • ISBN 9781032140650
  • Weight: 834g
  • Dimensions: 178 x 254mm
  • Publication Date: 31 Mar 2023
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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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.

Dr. Mir Masoom Ali is George and Frances Ball Distinguished Professor Emeritus of Statistics at Ball State University in the USA. His current research interest is in the area of G Families of probability distributions and he has published numerous papers in this area.

Dr. Irfan Ali is an Assistant Professor of Statistics at the Department of Statistics and Operations Research, Aligarh Muslim University, India. His current research areas are applied statistics and mathematical programming and he has published more than 100 research papers in these areas.

Dr. Haitham M. Yousof is an Assistant professor of Statistics at the Department of Statistics, Mathematics and Insurance, Benha University, Egypt. His current research areas are probability theory and G Families of probability distributions and he has published more than 200 research papers in these areas.

Dr. Mohamed Ibrahim is an Assistant professor of Statistics at the Department of Applied, Mathematical and Actuarial Statistics, Damietta University, Egypt. His current research areas are probability theory and G Families of probability distributions and he has published several research papers in these areas.