Birnbaum Saunders Distribution For Imprecise Data Statistical Properties Estimation Methods And Real Life Applications
Birnbaum Saunders Distribution For Imprecise Data Statistical Properties Estimation Methods And Real Life Applications Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Birnbaum Saunders Distribution For Imprecise Data Statistical Properties Estimation Methods And Real Life Applications book. This book definitely worth reading, it is an incredibly well-written.
Birnbaum Saunders distribution for imprecise data: statistical properties, estimation methods, and real life applications by Marwa K. Hassan,Muhammad Aslam Pdf
A neutrosophic statistic is a random variable and it has a neutrosophic probability distribution. So, in this paper, we introduce the new neutrosophic Birnbaum–Saunders distribution. Some statistical properties are derived, using Mathematica 13.1.1 and R-Studio Software. Two different estimation methods for parameters estimation are introduced for new distribution: maximum likelihood estimation method and Bayesian estimation method. A Monte-Carlo simulation study is used to investigate the behavior of parameters estimates of new distribution, compare the performance of different estimates, and compare between our distribution and the classical version of Birnbaum-Saunders. Finally, study the validity of our new distribution in real life.
The Birnbaum-Saunders Distribution by Victor Leiva Pdf
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.
Applied Statistical Methods covers the fundamental understanding of statistical methods necessary to deal with a wide variety of practical problems. This 14-chapter text presents the topics covered in a manner that stresses clarity of understanding, interpretation, and method of application. The introductory chapter illustrates the importance of statistical analysis. The next chapters introduce the methods of data summarization, including frequency distributions, cumulative frequency distributions, and measures of central tendency and variability. These topics are followed by discussions of the fundamental principles of probability, the concepts of sample spaces, outcomes, events, probability, independence of events, and the characterization of discrete and continuous random variables. Other chapters explore the distribution of several important statistics; statistical tests of hypotheses; point and interval estimation; and simple linear regression. The concluding chapters review the elements of single- and two-factor analysis of variance and the design of analysis of variance experiments. This book is intended primarily for advanced undergraduate and graduate students in the mathematical, physical, and engineering sciences, as well as in economics, business, and related areas. Researchers and line personnel in industry and government will find this book useful in self-study.
Information-Theoretic Methods for Estimating of Complicated Probability Distributions by Zhi Zong Pdf
Mixing up various disciplines frequently produces something that are profound and far-reaching. Cybernetics is such an often-quoted example. Mix of information theory, statistics and computing technology proves to be very useful, which leads to the recent development of information-theory based methods for estimating complicated probability distributions. Estimating probability distribution of a random variable is the fundamental task for quite some fields besides statistics, such as reliability, probabilistic risk analysis (PSA), machine learning, pattern recognization, image processing, neural networks and quality control. Simple distribution forms such as Gaussian, exponential or Weibull distributions are often employed to represent the distributions of the random variables under consideration, as we are taught in universities. In engineering, physical and social science applications, however, the distributions of many random variables or random vectors are so complicated that they do not fit the simple distribution forms at al. Exact estimation of the probability distribution of a random variable is very important. Take stock market prediction for example. Gaussian distribution is often used to model the fluctuations of stock prices. If such fluctuations are not normally distributed, and we use the normal distribution to represent them, how could we expect our prediction of stock market is correct? Another case well exemplifying the necessity of exact estimation of probability distributions is reliability engineering. Failure of exact estimation of the probability distributions under consideration may lead to disastrous designs. There have been constant efforts to find appropriate methods to determine complicated distributions based on random samples, but this topic has never been systematically discussed in detail in a book or monograph. The present book is intended to fill the gap and documents the latest research in this subject. Determining a complicated distribution is not simply a multiple of the workload we use to determine a simple distribution, but it turns out to be a much harder task. Two important mathematical tools, function approximation and information theory, that are beyond traditional mathematical statistics, are often used. Several methods constructed based on the two mathematical tools for distribution estimation are detailed in this book. These methods have been applied by the author for several years to many cases. They are superior in the following senses: (1) No prior information of the distribution form to be determined is necessary. It can be determined automatically from the sample; (2) The sample size may be large or small; (3) They are particularly suitable for computers. It is the rapid development of computing technology that makes it possible for fast estimation of complicated distributions. The methods provided herein well demonstrate the significant cross influences between information theory and statistics, and showcase the fallacies of traditional statistics that, however, can be overcome by information theory. Key Features: - Density functions automatically determined from samples - Free of assuming density forms - Computation-effective methods suitable for PC - density functions automatically determined from samples - Free of assuming density forms - Computation-effective methods suitable for PC
Distribution-Free Statistical Methods, Second Edition by J.S. Maritz Pdf
Distribution-free statistical methods enable users to make statistical inferences with minimum assumptions about the population in question. They are widely used, especially in the areas of medical and psychological research. This new edition is aimed at senior undergraduate and graduate level. It also includes a discussion of new techniques that have arisen as a result of improvements in statistical computing. Interest in estimation techniques has particularly grown, and this section of the book has been expanded accordingly. Finally, Distribution-Free Statistical Methods includes more examples with actual data sets appearing in the text.
Statistical Distributions by Nick T. Thomopoulos Pdf
This book gives a description of the group of statistical distributions that have ample application to studies in statistics and probability. Understanding statistical distributions is fundamental for researchers in almost all disciplines. The informed researcher will select the statistical distribution that best fits the data in the study at hand. Some of the distributions are well known to the general researcher and are in use in a wide variety of ways. Other useful distributions are less understood and are not in common use. The book describes when and how to apply each of the distributions in research studies, with a goal to identify the distribution that best applies to the study. The distributions are for continuous, discrete, and bivariate random variables. In most studies, the parameter values are not known a priori, and sample data is needed to estimate parameter values. In other scenarios, no sample data is available, and the researcher seeks some insight that allows the estimate of the parameter values to be gained. This handbook of statistical distributions provides a working knowledge of applying common and uncommon statistical distributions in research studies. These nineteen distributions are: continuous uniform, exponential, Erlang, gamma, beta, Weibull, normal, lognormal, left-truncated normal, right-truncated normal, triangular, discrete uniform, binomial, geometric, Pascal, Poisson, hyper-geometric, bivariate normal, and bivariate lognormal. Some are from continuous data and others are from discrete and bivariate data. This group of statistical distributions has ample application to studies in statistics and probability and practical use in real situations. Additionally, this book explains computing the cumulative probability of each distribution and estimating the parameter values either with sample data or without sample data. Examples are provided throughout to guide the reader. Accuracy in choosing and applying statistical distributions is particularly imperative for anyone who does statistical and probability analysis, including management scientists, market researchers, engineers, mathematicians, physicists, chemists, economists, social science researchers, and students in many disciplines.
Probability Distributions Used in Reliability Engineering by Andrew N O'Connor Pdf
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.
Neutrosophic Sets and Systems by Florentin Smarandache,Surapati Pramanik Pdf
“Neutrosophic Sets and Systems” has been created for publications on advanced studies in neutrosophy, neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics that started in 1995 and their applications in any field, such as the neutrosophic structures developed in algebra, geometry, topology, etc.
Recurrent Events Data Analysis for Product Repairs, Disease Recurrences, and Other Applications by Wayne B. Nelson Pdf
Survival data consist of a single event for each population unit, namely, end of life, which is modeled with a life distribution. However, many applications involve repeated-events data, where a unit may accumulate numerous events over time. This applied book provides practitioners with basic nonparametric methods for such data.
An Introduction to Bartlett Correction and Bias Reduction by Gauss M. Cordeiro,Francisco Cribari-Neto Pdf
This book presents a concise introduction to Bartlett and Bartlett-type corrections of statistical tests and bias correction of point estimators. The underlying idea behind both groups of corrections is to obtain higher accuracy in small samples. While the main focus is on corrections that can be analytically derived, the authors also present alternative strategies for improving estimators and tests based on bootstrap, a data resampling technique and discuss concrete applications to several important statistical models.
Neutrosophic Probability, Set, And Logic (first version) by FLORENTIN SMARANDACHE Pdf
This project is a part of a National Science Foundation interdisciplinary project proposal. Starting from a new viewpoint in philosophy, the neutrosophy, one extends the classical "probability theory", "fuzzy set" and "fuzzy logic" to , and respectively.
Statistical Quality Technologies by Yuhlong Lio,Hon Keung Tony Ng,Tzong-Ru Tsai,Ding-Geng Chen Pdf
This book explores different statistical quality technologies including recent advances and applications. Statistical process control, acceptance sample plans and reliability assessment are some of the essential statistical techniques in quality technologies to ensure high quality products and to reduce consumer and producer risks. Numerous statistical techniques and methodologies for quality control and improvement have been developed in recent years to help resolve current product quality issues in today’s fast changing environment. Featuring contributions from top experts in the field, this book covers three major topics: statistical process control, acceptance sampling plans, and reliability testing and designs. The topics covered in the book are timely and have a high potential impact and influence to academics, scholars, students and professionals in statistics, engineering, manufacturing and health.
Probabilistic Reliability Engineering by Boris Gnedenko,Igor A. Ushakov Pdf
With the growing complexity of engineered systems, reliability hasincreased in importance throughout the twentieth century. Initiallydeveloped to meet practical needs, reliability theory has become anapplied mathematical discipline that permits a priori evaluationsof various reliability indices at the design stages. Theseevaluations help engineers choose an optimal system structure,improve methods of maintenance, and estimate the reliability on thebasis of special testing. Probabilistic Reliability Engineeringfocuses on the creation of mathematical models for solving problemsof system design. Broad and authoritative in its content, Probabilistic ReliabilityEngineering covers all mathematical models associated withprobabilistic methods of reliability analysis, including--unique tothis book--maintenance and cost analysis, as well as many newresults of probabilistic testing. To provide readers with all necessary background material, thistext incorporates a thorough review of the fundamentals ofprobability theory and the theory of stochastic processes. Itoffers clear and detailed treatment of reliability indices, thestructure function, load-strength reliability models, distributionswith monotone intensity functions, repairable systems, the Markovmodels, analysis of performance effectiveness, two-pole networks,optimal redundancy, optimal technical diagnosis, and heuristicmethods in reliability. Throughout the text, an abundance of realworld examples and case studies illustrate and illuminate thetheoretical points under consideration. For engineers in design, operations research, and maintenance, aswell as cost analysts and R&D managers, ProbabilisticReliability Engineering offers the most lucid, comprehensivetreatment of the subject available anywhere. About the editor JAMES A. FALK is Professor and Chairman of the Department ofOperations Research at George Washington University. In addition tohis numerous publications, Dr. Falk has lectured internationally asa Fulbright Lecturer. Of related interest... The reliability-testing "bible" for three generations of EasternEuropean scientists, adapted for Western scientists andengineers... HANDBOOK OF RELIABILITY ENGINEERING Originally published in the USSR, Handbook of ReliabilityEngineering set the standard for the reliability testing oftechnical systems for nearly three generations of appliedscientists and engineers. Authored by a group of prominent Sovietspecialists in reliability, it provides professionals and studentswith a comprehensive reference covering mathematical formulas andtechniques for incorporating reliability into engineering designsand testing procedures. Divided into twenty-four self-containedchapters, the Handbook details reliability fundamentals, examinescommon reliability problems and solutions, provides a collection ofcomputation formulas, and illustrates practical applications. The Handbook's Russian editor and internationally recognized expertIgor A. Ushakov has joined with American engineering professionalsto bring this indispensable resource to English-speaking engineersand scientists. 1994 (0-471-57173-3) 663 pp.
The Gradient Test: Another Likelihood-Based Test presents the latest on the gradient test, a large-sample test that was introduced in statistics literature by George R. Terrell in 2002. The test has been studied by several authors, is simply computed, and can be an interesting alternative to the classical large-sample tests, namely, the likelihood ratio (LR), Wald (W), and Rao score (S) tests. Due to the large literature about the LR, W and S tests, the gradient test is not frequently used to test hypothesis. The book covers topics on the local power of the gradient test, the Bartlett-corrected gradient statistic, the gradient statistic under model misspecification, and the robust gradient-type bounded-influence test. Covers the background of the gradient statistic and the different models Discusses The Bartlett-corrected gradient statistic Explains the algorithm to compute the gradient-type statistic
Scale Effect and Anisotropy Analyzed for Neutrosophic Numbers of Rock Joint Roughness Coefficient Based on Neutrosophic Statistics by Jiqian Chen ,Jun Ye , Shigui Du Pdf
In rock mechanics, the study of shear strength on the structural surface is crucial to evaluating the stability of engineering rock mass. In order to determine the shear strength, a key parameter is the joint roughness coefficient (JRC).
