The Birnbaum Saunders Distribution
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The Birnbaum Saunders Distribution
Author | : Victor Leiva |
Publsiher | : Academic Press |
Total Pages | : 154 |
Release | : 2015-10-26 |
Genre | : Mathematics |
ISBN | : 9780128038277 |
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The Birnbaum-Saunders Distribution presents the statistical theory, methodology, and applications of the Birnbaum-Saunders distribution, a very flexible distribution for modeling different types of data (mainly lifetime data). The book describes the most recent theoretical developments of this model, including properties, transformations and related distributions, lifetime analysis, and shape analysis. It discusses methods of inference based on uncensored and censored data, goodness-of-fit tests, and random number generation algorithms for the Birnbaum-Saunders distribution, also presenting existing and future applications. Introduces inference in the Birnbaum-Saunders distribution Provides a comprehensive review of the statistical theory and methodology of the Birnbaum-Distribution Discusses different applications of the Birnbaum-Saunders distribution Explains characterization and the lifetime analysis
The Birnbaum Saunders Distribution
Author | : Victor Leiva |
Publsiher | : Academic Press |
Total Pages | : 0 |
Release | : 2015-10-22 |
Genre | : Mathematics |
ISBN | : 0128037695 |
Download The Birnbaum Saunders Distribution Book in PDF, Epub and Kindle
The Birnbaum-Saunders Distribution presents the statistical theory, methodology, and applications of the Birnbaum-Saunders distribution, a very flexible distribution for modeling different types of data (mainly lifetime data). The book describes the most recent theoretical developments of this model, including properties, transformations and related distributions, lifetime analysis, and shape analysis. It discusses methods of inference based on uncensored and censored data, goodness-of-fit tests, and random number generation algorithms for the Birnbaum-Saunders distribution, also presenting existing and future applications.
Robust Estimation of the Birnbaum Saunders Distribution
Author | : Joanna E. Mills,Technical University of Nova Scotia. Department of Applied Mathematics |
Publsiher | : Unknown |
Total Pages | : 306 |
Release | : 1997 |
Genre | : Electronic Book |
ISBN | : OCLC:229162415 |
Download Robust Estimation of the Birnbaum Saunders Distribution Book in PDF, Epub and Kindle
Robust Estimation of the Birnbaum Saunders Distribution
Author | : Joanna E. Mills |
Publsiher | : Unknown |
Total Pages | : 0 |
Release | : 1997 |
Genre | : Electronic Book |
ISBN | : OCLC:229162415 |
Download Robust Estimation of the Birnbaum Saunders Distribution Book in PDF, Epub and Kindle
Symmetric and Asymmetric Distributions
Author | : Emilio Gómez Déniz |
Publsiher | : MDPI |
Total Pages | : 146 |
Release | : 2021-01-21 |
Genre | : Social Science |
ISBN | : 9783039366460 |
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In recent years, the advances and abilities of computer software have substantially increased the number of scientific publications that seek to introduce new probabilistic modelling frameworks, including continuous and discrete approaches, and univariate and multivariate models. Many of these theoretical and applied statistical works are related to distributions that try to break the symmetry of the normal distribution and other similar symmetric models, mainly using Azzalini's scheme. This strategy uses a symmetric distribution as a baseline case, then an extra parameter is added to the parent model to control the skewness of the new family of probability distributions. The most widespread and popular model is the one based on the normal distribution that produces the skewed normal distribution. In this Special Issue on symmetric and asymmetric distributions, works related to this topic are presented, as well as theoretical and applied proposals that have connections with and implications for this topic. Immediate applications of this line of work include different scenarios such as economics, environmental sciences, biometrics, engineering, health, etc. This Special Issue comprises nine works that follow this methodology derived using a simple process while retaining the rigor that the subject deserves. Readers of this Issue will surely find future lines of work that will enable them to achieve fruitful research results.
First Hitting Time Regression Models
Author | : Chrysseis Caroni |
Publsiher | : John Wiley & Sons |
Total Pages | : 200 |
Release | : 2017-07-17 |
Genre | : Mathematics |
ISBN | : 9781119437222 |
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This book aims to promote regression methods for analyzing lifetime (or time-to-event) data that are based on a representation of the underlying process, and are therefore likely to offer greater scientific insight compared to purely empirical methods. In contrast to the rich statistical literature, the regression methods actually employed in lifetime data analysis are limited, particularly in the biomedical field where D. R. Cox’s famous semi-parametric proportional hazards model predominates. Practitioners should become familiar with more flexible models. The first hitting time regression models (or threshold regression) presented here represent observed events as the outcome of an underlying stochastic process. One example is death occurring when the patient’s health status falls to zero, but the idea has wide applicability – in biology, engineering, banking and finance, and elsewhere. The central topic is the model based on an underlying Wiener process, leading to lifetimes following the inverse Gaussian distribution. Introducing time-varying covariates and many other extensions are considered. Various applications are presented in detail.
Progressive Censoring
Author | : N. Balakrishnan,Rita Aggarwala |
Publsiher | : Springer Science & Business Media |
Total Pages | : 255 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 9781461213345 |
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This new book offers a guide to the theory and methods of progressive censoring. In many industrial experiments involving lifetimes of machines or units, experiments have to be terminated early. Progressive Censoring first introduces progressive sampling foundations, and then discusses various properties of progressive samples. The book points out the greater efficiency gained by using this scheme instead of classical right-censoring methods.
Testing and Inspection Using Acceptance Sampling Plans
Author | : Muhammad Aslam,Mir Masoom Ali |
Publsiher | : Springer |
Total Pages | : 288 |
Release | : 2019-07-19 |
Genre | : Mathematics |
ISBN | : 9789811393068 |
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This book introduces a number of new sampling plans, such as time truncated life tests, skip sampling plans, resubmitted plans, mixed sampling plans, sampling plans based on the process capability index and plans for big data, which can be used for testing and inspecting products, from the raw-materials stage to the final product, in every industry using statistical process control techniques. It also presents the statistical theory, methodology and applications of acceptance sampling from truncated life tests. Further, it discusses the latest reliability, quality and risk analysis methods based on acceptance sampling from truncated life, which engineering and statisticians require in order to make decisions, and which are also useful for researchers in the areas of quality control, lifetime analysis, censored data analysis, goodness-of-fit and statistical software applications. In its nine chapters, the book addresses a wide range of testing/inspection sampling schemes for discrete and continuous data collected in various production processes. It includes a chapter on sampling plans for big data and offers several illustrative examples of the procedures presented. Requiring a basic knowledge of probability distributions, inference and estimation, and lifetime and quality analysis, it is a valuable resource for graduate and senior undergraduate engineering students, and practicing engineers, more specifically it is useful for quality engineers, reliability engineers, consultants, black belts, master black belts, students and researchers interested in applying reliability and risk and quality methods.