"This text emphasizes nonlinear models for a course in time series analysis. After introducing stochastic processes, Markov chains, Poisson processes, and ARMA models, the authors cover functional autoregressive, ARCH, threshold AR, and discrete time series models as well as sev…
Front Cover; Contents; Preface; Chapter 1 Science in Perspective; Chapter 2 Formal Reasoning; Chapter 3 Experimentation; Chapter 4 Scientific Inference; Chapter 5 Dynamics of Science; Chapter 6 Controversies and Challenges; Chapter 7 Case Studies; Bibliography; Back Cover.
This book is dedicated to metaheuristics as applied to vehicle routing problems. Several implementations are given as illustrative examples, along with applications to several typical vehicle routing problems. As a first step, a general presentation intends to make the reader mo…
"While most mathematical examples illustrate the truth of a statement, counterexamples demonstrate a statement's falsity. Enjoyable topics of study, counterexamples are valuable tools for teaching and learning. The definitive book on the subject in regards to probability, this t…
"The essential guide for data scientists and for leaders who must get more from their data science teams. The Economist boldly claims that data are now 'the world's most valuable resource.' But, as Kenett and Redman so richly describe, unlocking that value requires far more than…
Stochastic processes occur everywhere in the sciences, economics and engineering, and they need to be understood by (applied) mathematicians, engineers and scientists alike. This book gives a gentle introduction to Brownian motion and stochastic processes, in general. Brownian m…
BackgroundOne-Sample Nonparametric InferenceTwo-Sample TestingMethods for Three or More GroupsGroup Differences with BlockingBivariate MethodsMultivariate AnalysisDensity EstimationRegression Function EstimatesResampling TechniquesAppendices. An Introduction to Nonparametric St…
An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as…
