Offers an applications-oriented treatment of parameter estimation from both complete and censored samples; contains notations, simplified formats for estimates, graphical techniques, and numerous tables and charts allowing users to calculate estimates and analyze sample data quickly and easily. Anno
Reliability is an essential concept in mathematics, computing, research, and all disciplines of engineering, and reliability as a characteristic is, in fact, a probability. Therefore, in this book, the author uses the statistical approach to reliability modelling along with the MINITAB software package to provide a comprehensive treatment of modelling, from the basics through advanced modelling techniques.The book begins by presenting a thorough grounding in the elements of modelling the lifetime of a single, non-repairable unit. Assuming no prior knowledge of the subject, the author includes a guide to all the fundamentals of probability theory, defines the various measures associated with reliability, then describes and discusses the more common lifetime models: the exponential, Weibull, normal, lognormal and gamma distributions. She concludes the groundwork by looking at ways of choosing and fitting the most appropriate model to a given data set, paying particular attention to two critical points: the effect of censored data and estimating lifetimes in the tail of the distribution.The focus then shifts to topics somewhat more difficult:the difference in the analysis of lifetimes for repairable versus non-repairable systems and whether repair truly ""renews"" the systemmethods for dealing with system with reliability characteristic specified for more than one component or subsystemthe effect of different types of maintenance strategiesthe analysis of life test dataThe final chapter provides snapshot introductions to a range of advanced models and presents two case studies that illustrate various ideas from throughout the book.
"Models of physical systems are originally in continuous-time (CT) since they are based on physical laws which are invariably in CT. To identify these models directly in their native CT form, avoiding intermediate discrete time (DT) model identification, the established method consists of two stages. In the primary stage signals are processed by a general linear dynamic operation- computational or realized through prefilters to preserve the system parameters in their native continuous time form and the literature is rich on this aspect. This book complements the existing literature about identification of continuous-time systems by enhancing the secondary stage by considering models which are linear in unknown parameters thereby rendering estimation linear and robust"--
A technically precise yet clear presentation of modern sequential methodologies having immediate applications to practical problems in the real world, Applied Sequential Methodologies communicates invaluable techniques for data mining, agricultural science, genetics, computer simulation, finance, clinical trials, sonar signal detection, randomizati
A milestone in the published literature on the subject, this first-ever Handbook of Beta Distribution and Its Applications clearly enumerates the properties of beta distributions and related mathematical notions. It summarizes modern applications in a variety of fields, reviews up-and-coming progress from the front lines of statistical research and
Based on a loss function approach, this comprehensive reference reviews the most recent advances in financial and actuarial modeling, providing a strong statistical background for advanced methods in pension plan structuring, risk estimation, and modeling of investment and options pricing. An authoritative tool supplying every conceptual model and
Provides a unified account of the most popular approaches to nonparametric regression smoothing. This edition contains discussions of boundary corrections for trigonometric series estimators; detailed asymptotics for polynomial regression; testing goodness-of-fit; estimation in partially linear models; practical aspects, problems and methods for co
The exponential distribution is one of the most significant and widely used distribution in statistical practice. It possesses several important statistical properties, and yet exhibits great mathematical tractability. This volume provides a systematic and comprehensive synthesis of the diverse literature on the theory and applications of the expon
Thoroughly revised and reorganized, the fourth edition presents in-depth coverage of the theory and methods of the most widely used nonparametric procedures in statistical analysis and offers example applications appropriate for all areas of the social, behavioral, and life sciences. The book presents new material on the quantiles, the calculation of exact and simulated power, multiple comparisons, additional goodness-of-fit tests, methods of analysis of count data, and modern computer applications using MINITAB, SAS, and STATXACT. It includes tabular guides for simplified applications of tests and finding P values and confidence interval estimates.
This work details the statistical inference of linear models including parameter estimation, hypothesis testing, confidence intervals, and prediction. The authors discuss the application of statistical theories and methodologies to various linear models such as the linear regression model, the analysis of variance model, the analysis of covariance model, and the variance components model.