Characterization and Identification of Probability Distributions


Book Description

This book introduces three new criteria (a) moment relations, (b) moment ratios and (c) ratios of the coefficients of the recurrence relations to characterize and indentify probability distributions. In general moment relations criteria are effective but there are some special situations, where the moment relations of two or more suspected distributions are same or one particular moment function takes same value for two or more distributions. In such a situation two moment ratios as extra criteria are proposed for deciding among them. This book also discussed the identification of a distribution by using the ratios of the coefficients of the recurrence relations obtained from its generating function. The significant contribution of this research is to introduce a new special class of exponential family of distributions named 'transformed Chi-square family." Explicit expressions for the MVUE with MV of a function of the parameter of this family are given. The critical region and the power function for various tests of hypotheses for the parameter of this family are also obtained. An identification procedure with probability of correct identification is discussed in detail.







Probability Distributions Used in Reliability Engineering


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.




Characterizations of Some Discrete Distributions


Book Description

The characterization of distribution is useful for selection of adequate distribution to describe the observed values obtained in an experiment and is one of the methods of finding the distribution. Chapter 3 and 4 are concerned with the characterization developed by Kemp and Kemp (2004) and Ahmad and Roohi (2004). In Chapter 5, the recurrence relations between ordinary moments are established. A general characterization theorem, based on recurrence relation of ordinary moments is derived for a general class of discrete distributions. Chapter 6 deals with the recursive relations of factorial moments obtained by successive differentiation of factorial moment generating functions. In Chapters 7, 8, and 9 the theorems are then applied to numerous discrete probability distributions to provide specific characterizations for each one of them. Since information concerning moments is more often available than the knowledge of probability distribution as a whole, we expect these properties to be useful in dealing with the practical problems.




The Theory of Canonical Moments with Applications in Statistics, Probability, and Analysis


Book Description

This new material is concerned with the theory and applications of probability, statistics and analysis of canonical moments. It provides a powerful tool for the determination of optimal experimental designs, for the calculation of the main characteristics of random walks, and for other moment problems appearing in probability and statistics.




Elements of Probability Theory


Book Description

Elements of Probability Theory focuses on the basic ideas and methods of the theory of probability. The book first discusses events and probabilities, including the classical meaning of probability, fundamental properties of probabilities, and the primary rule for the multiplication of probabilities. The text also touches on random variables and probability distributions. Topics include discrete and random variables; functions of random variables; and binomial distributions. The selection also discusses the numerical characteristics of probability distributions; limit theorems and estimates of the mean; and the law of large numbers. The text also describes linear correlation, including conditional expectations and their properties, coefficient of correlation, and best linear approximation to the regression function. The book presents tables that show the values of the normal probability integral, Poisson distribution, and values of the normal probability density. The text is a good source of data for readers and students interested in probability theory.







Univariate Discrete Distributions


Book Description

This Set Contains: Continuous Multivariate Distributions, Volume 1, Models and Applications, 2nd Edition by Samuel Kotz, N. Balakrishnan and Normal L. Johnson Continuous Univariate Distributions, Volume 1, 2nd Edition by Samuel Kotz, N. Balakrishnan and Normal L. Johnson Continuous Univariate Distributions, Volume 2, 2nd Edition by Samuel Kotz, N. Balakrishnan and Normal L. Johnson Discrete Multivariate Distributions by Samuel Kotz, N. Balakrishnan and Normal L. Johnson Univariate Discrete Distributions, 3rd Edition by Samuel Kotz, N. Balakrishnan and Normal L. Johnson Discover the latest advances in discrete distributions theory The Third Edition of the critically acclaimed Univariate Discrete Distributions provides a self-contained, systematic treatment of the theory, derivation, and application of probability distributions for count data. Generalized zeta-function and q-series distributions have been added and are covered in detail. New families of distributions, including Lagrangian-type distributions, are integrated into this thoroughly revised and updated text. Additional applications of univariate discrete distributions are explored to demonstrate the flexibility of this powerful method. A thorough survey of recent statistical literature draws attention to many new distributions and results for the classical distributions. Approximately 450 new references along with several new sections are introduced to reflect the current literature and knowledge of discrete distributions. Beginning with mathematical, probability, and statistical fundamentals, the authors provide clear coverage of the key topics in the field, including: Families of discrete distributions Binomial distribution Poisson distribution Negative binomial distribution Hypergeometric distributions Logarithmic and Lagrangian distributions Mixture distributions Stopped-sum distributions Matching, occupancy, runs, and q-series distributions Parametric regression models and miscellanea Emphasis continues to be placed on the increasing relevance of Bayesian inference to discrete distribution, especially with regard to the binomial and Poisson distributions. New derivations of discrete distributions via stochastic processes and random walks are introduced without unnecessarily complex discussions of stochastic processes. Throughout the Third Edition, extensive information has been added to reflect the new role of computer-based applications. With its thorough coverage and balanced presentation of theory and application, this is an excellent and essential reference for statisticians and mathematicians.