News Release 

Discussing reliable lifetime data analysis

Bentham Science Publishers

Mathematicians and statisticians have made significant academic progress on the subject of distribution theory in the last two decades, and this area of study is becoming one of the main statistical tools for the analysis of lifetime (survival) data. In many ways, lifetime distributions are the common language of survival dialogue because the framework subsumes many statistical properties of interest, such as reliability, entropy and maximum likelihood.

Recent Advances in Lifetime and Reliability Models provides a comprehensive account of models and methods for lifetime models. Building from primary definitions such as density and hazard rate functions, this book presents readers a broad framework on distribution theory in survival analysis. This framework covers classical methods - such as the exponentiated distribution method - as well as recent models explaining lifetime distributions, such as the beta family and compounding models. Additionally, a detailed discussion of mathematical and statistical properties of each family, such as mixture representations, asymptotes, types of moments, order statistics, quantile functions, generating functions and estimation is presented in the book.

Key Features:

  • Presents information about classical and modern lifetime methods
  • Covers key properties of different models in detail
  • Explores regression models for the beta generalized family of distributions
  • Focuses information on both theoretical fundamentals and practical aspects of implementing different models
  • Features examples relevant to business engineering and biomedical sciences

Recent Advances in Lifetime and Reliability Models will equip students, researchers and working professionals with the information to make extensive use of observational data in a variety of fields to create inferential models that make sense of lifetime data.


About the Editors:

Gauss M. Cordeiro received a Ph.D. in Statistics from the Imperial College of Science and Technology, University of London. Currently, Dr. Cordeiro is a Class A researcher of the Brazilian Research Council-CNPq, full professor at the Federal University of Pernambuco (Brazil) and a Member of the Graduate Program in Statistics at the same university. He served as the president of the Associacrao Brasileira de Estatistica, 2000-2002 and was one of the founding editors and editor-in-chief of the Brazilian Journal of Probability and Statistics. In 2010, Dr. Cordeiro was awarded the National Medal for Scientific Merit from the Brazilian Government at the Order of Comendador. He is a member of the Academy of Sciences of Pernambuco (Brazil). Dr. Cordeiro's main research interests in statistics include asymptotic theory, probability distributions and regression models. He has published more than 410 papers in international statistical journals.

Rodrigo B. Silva received his D.Sc. in Statistics from Universidade Federal de Pernambuco and joined the Departamento de Estatistica da Universidade Federal da Paraiiba. Dr. Silva is a periodic reviewer of several scientific journals, including Computational Statistics and Data Analysis and Reliability Engineering and System Safety, Mathematics and Computers in Simulation. With a background in several areas of statistics, his recent research focuses on the time series analysis, reliability engineering, survival analysis, distribution theory and regression models.

Abrado D. C. Nascimento received B.Sc. M.Sc. and D.Sc. education in statistics from Universidade Federal de Pernambuco (UFPE), Brazil and joined the Department of Statistics at Universidade Federal da Paraiba. Currently, Dr. Nascimento is an adjunct professor of the Statistics Department at UFPE. His research interests are statistical information theory, higher order asymptotic theory, inference on random matrices (applied to polarimetric synthetic aperture radar imagery), time series analysis, manifolds on Statistics (directed to the statistical theory of shape) and survival analysis.

Keywords: distribution theory, lifetime models, survival analysis, regression models, observational data, hazard rate functions, Mathematics, Statistics

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