This book offers comprehensive information on the theory, models and algorithms involved in state-of-the-art multivariate time series analysis and highlights several of the latest research advances in climate and environmental science.
This book comprehensively presents the basic concepts of probability and Bayesian inference with sufficient generality to make them applicable to current problems in scientific research.
How to Validate a Pharmaceutical Process provides a "e;how to approach to developing and implementing a sustainable pharmaceutical process validation program.
This book covers methods of applied statistics for researchers who design and conduct experiments, perform statistical inference, and write technical reports.
A comprehensive and accessible presentation of probability and stochastic processes with emphasis on key theoretical concepts and real-world applications With a sophisticated approach, Probability and Stochastic Processes successfully balances theory and applications in a pedagogical and accessible format.
A complete introduction to discriminant analysis--extensively revised, expanded, and updated This Second Edition of the classic book, Applied Discriminant Analysis, reflects and references current usage with its new title, Applied MANOVA and Discriminant Analysis.
This volume includes new topics such as the stochastic limit approach to nonequilibrium states, a new algebraic approach to relativistic nonequilibrium local states, classical and quantum features of weak chaos, transports in quantum billiards, the Welcher-Weg puzzle with a decaying atom, and the topics related to the quantum Zeno effect.
A preeminent expert in the field explores new and exciting methodologies in the ever-growing field of robust statistics Used to develop data analytical methods, which are resistant to outlying observations in the data, while capable of detecting outliers, robust statistics is extremely useful for solving an array of common problems, such as estimating location, scale, and regression parameters.
A Strong Practical Focus on Applications and AlgorithmsComputational Statistics Handbook with MATLAB, Third Edition covers today's most commonly used techniques in computational statistics while maintaining the same philosophy and writing style of the bestselling previous editions.
Piecewise-deterministic Markov processes form a class of stochastic models with a sizeable scope of applications: biology, insurance, neuroscience, networks, finance.
An in-depth look at the intersection of judgment and statistics in baseballScouting and scoring are considered fundamentally different ways of ascertaining value in baseball.
Presenting a strong and clear relationship between theory and practice, Linear and Integer Optimization: Theory and Practice is divided into two main parts.
On the manufacturing shop floor, the principle of "e;value comes from the production of parts rather than charts"e; crucially applies when using practical statistical process control (SPC).
This book constitutes the proceedings of the 12th International Workshop on Algorithms and Computation, WALCOM 2018, held in Dhaka, Bangladesh, in March 2018.
Emphasizing concepts rather than recipes, An Introduction to Statistical Inference and Its Applications with R provides a clear exposition of the methods of statistical inference for students who are comfortable with mathematical notation.
This book constitutes the thoroughly refereed post-conference proceedings of the 10th International Conference on Quantum Interaction, QI 2018, held in Nice, France, in September 2018.
This book provides, as simply as possible, sound foundations for an in-depth understanding of reliability engineering with regard to qualitative analysis, modelling, and probabilistic calculations of safety and production systems.
This thesis advances our understanding of how thermal anisotropy can be exploited to extract work through a mechanism that is quite distinct from the classical Carnot heat engine.
Since about 1915 integration theory has consisted of two separate branches: the abstract theory required by probabilists and the theory, preferred by analysts, that combines integration and topology.
Design of Experiments in Nonlinear Models: Asymptotic Normality, Optimality Criteria and Small-Sample Properties provides a comprehensive coverage of the various aspects of experimental design for nonlinear models.
Although portfolio management didn't change much during the 40 years after the seminal works of Markowitz and Sharpe, the development of risk budgeting techniques marked an important milestone in the deepening of the relationship between risk and asset management.
The purpose of this book is to provide the reader with a solid background and understanding of the basic results and methods in probability theory before entering into more advanced courses (in probability and/or statistics).
This book introduces and explains the parametric accelerated life testing (ALT) methodology as a new reliability methodology based on statistics, to help avoid recalls of products in the marketplace.
Created in partnership with SAS, this book explores SAS, a business intelligence software that can be used in any business setting or enterprise for data delivery, reporting, data mining, forecasting, statistical analysis, and more SAS employee and technologist Stephen McDaniel combines real-world expertise and a friendly writing style to introduce readers to SAS basics Covers crucial topics such as getting various types of data into the software, producing reports, working with the data, basic SAS programming, macros, and working with SAS and databases
New ideas on the mathematical foundations of quantum mechanics, related to the theory of quantum measurement, as well as the emergence of quantum optics, quantum electronics and optical communications have shown that the statistical structure of quantum mechanics deserves special investigation.
This book contains the latest material in the subject, covering next generation sequencing (NGS) applications and meeting the requirements of a complete semester course.
A properly structured financial model can provide decision makers with a powerful planning tool that helps them identify the consequences of their decisions before they are put into practice.
