Functional Analysis Methods for Reliability Models

Simplifies the treatment of statistical inference focusing on how to specify and interpret models in the context of testing causal theories. Simple bivariate regression, multiple regression, multiple

Featuring aerospace examples and applications, Reliability Analysis of Dynamic Systems presents the very latest probabilistic techniques for accurate and efficient dynamic system reliability analysis.

Through simple, practical approaches, Reliability Analysis and Prediction with Warranty Data: Issues, Strategies, and Methods helps Six Sigma black belts and engineers successfully interpret warranty

The volume contains the proceedings of the Conference on Operator Theory and its Applications, held in Gothenburg, Sweden, April 26-29, 2011, in honour of Professor Victor Shulman, on the occasion of

This handbook represents a milestone in the progression of Data Envelopment Analysis (DEA). Written by experts who are often major contributors to DEA theory, it includes a collection of chapters that

Everyone working in related fields from applied mathematicians to statisticians to actuaries and operations researchers will find this a brilliantly useful practical text. The book presents applicatio

Reliability, Maintainability and Risk 9th Edition has taught reliability and safety engineers techniques to minimize process design and operation defects and failures for 35 years. For beginners, the

For over 30 years, Reliability, Maintainability and Risk has been recognised as a leading text for reliability and maintenance professionals. Now in its seventh edition, the book has been updated t

A unique, practical guide for industry professionals who need to improve product quality and reliability in repairable systemsOwing to its vital role in product quality, reliability has been intensely

This book extends our understanding of how different cross-functional business and management disciplines, such as innovation and entrepreneurship, strategic management, marketing and HRM, individuall

Pons (Institute of Agronomic Research, France) presents new mathematical results about statistical methods for the density and regression functions, which appear widely in the mathematical literature.

A vital text that collates a wealth of previously dispersed material on the derivation and analysis of fluid models in plasma physics, this stimulating introduction to the topic deploys rigorous asymp

The book begins with an in-depth survey of coagulation-fragmentation models, followed by a detailed presentation of relevant earlier results in the field. The mathematical tools necessary for the mode

This text emphasizes the general area of nonlinear engineering models with an in-depth discussion of computer algorithms for solving nonlinear problems coupled with appropriate examples of such nonli

Stochastic order relations prprovide a valuable insight into the behaviour of complex stochastic (random) systems and enable the user to collect meaningful comparative data. Application areas include

A practical guide to the analysis of models in mathematical biology and the biomedical sciences, this book utilizes MATLAB® to investigate the methodology for collecting, visualizing, and analyzing ex

This book provides a concise and meticulous introduction to functional analysis. Since the topic draws heavily on the interplay between the algebraic structure of a linear space and the distance struc

This brief treats dynamical systems that involve delays and random disturbances. The study is motivated by a wide variety of systems in real life in which random noise has to be taken into considerati

This book provides a concise and meticulous introduction to functional analysis. Since the topic draws heavily on the interplay between the algebraic structure of a linear space and the distance struc

Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a

This book presents distributed systems from a designer's point-of-view. First, it gives examples of systems taken from industrial case studies. Then, it suggests how to model them from system requirem

A thorough review of the most current regression methods in time series analysis Regression methods have been an integral part of time series analysis for over a century. Recently, new developments ha

Broadening its scope to nonstatisticians, Bayesian Methods for Data Analysis, Third Edition provides an accessible introduction to the foundations and applications of Bayesian analysis. Along with a c

Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.

Provides the basic skills and information required to prepare an environmental sample for analysis. Divided into two sections, i.e. Inorganic Analysis and Organic Analysis, this book covers selected t