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Mathematical Modeling in Nutrition and the Health Sciences
This volume is the proceedings of the 7th Mathematical Modeling in Experimental Nutrition Conference held at Penn State University July 29 until August 1, 2000. The book addresses the determination of optimal intakes of nutrients and food components to provide lifelong health and reduce incidence of disease. Mathematical modelling provides a means of rigorously defining the functions of a system and using a variety of conditions to stimulate responses. This volume presents the newest advances in modelling and related experimental techniques required to meet the new challenges currently facing nutrition and biological science.
Volterra and Functional Differential Equations
This book contains twenty four papers, presented at the conference on Volterra and Functional Differential Equations held in Virginia in 1981, on various topics, including Liapunov stability, Volterra equations, integral equations, and functional differential equations.
The Optimal Design of Blocked and Split-Plot Experiments
This book provides a comprehensive treatment of the design of blocked and split-plot experiments. The optimal design approach advocated in the book will help applied statisticians from industry, medicine, agriculture, chemistry and many other fields of study in setting up tailor-made experiments. The book also contains a theoretical background, a thorough review of the recent work in the area of blocked and split-plot experiments, and a number of interesting theoretical results.
Computer Modeling and Simulations of Complex Biological Systems, 2nd Edition
This unique text explores the use of innovative modeling techniques in effecting a better understanding of complex diseases such as AIDS and cancer. From a way of representing the computational properties of protein-folding problems to computer simulation of bimodal neurons and networks, Computer Modeling and Simulations of Complex Biological Systems examines several modeling methodologies and integrates them across a variety of disciplines. This interdisciplinary approach suggests new ways to solve complex problems pertaining to biological systems. Written in clear and simple terms appropriate for both the novice and the experienced researcher, the book presents a step-by-step approach to the subject and includes numerous examples that explain the concepts presented in the text.
Benchmarking, Temporal Distribution, and Reconciliation Methods for Time Series
Time series play a crucial role in modern economies at all levels of activity and are used by decision makers to plan for a better future. Before publication time series are subject to statistical adjustments and this is the first statistical book to systematically deal with the methods most often applied for such adjustments. Regression-based models are emphasized because of their clarity, ease of application, and superior results. Each topic is illustrated with real case examples. In order to facilitate understanding of their properties and limitations of the methods discussed a real data example is followed throughout the book.
Space, Structure and Randomness
Space, structure, and randomness: these are the three key concepts underlying Georges Matheron’s scientific work. He first encountered them at the beginning of his career when working as a mining engineer, and then they resurfaced in fields ranging from meteorology to microscopy. What could these radically different types of applications possibly have in common? First, in each one only a single realisation of the phenomenon is available for study, but its features repeat themselves in space; second, the sampling pattern is rarely regular, and finally there are problems of change of scale. This volume is divided in three sections on random sets, geostatistics and mathematical morphology. Th...
Multivariate Nonparametric Methods with R
This book offers a new, fairly efficient, and robust alternative to analyzing multivariate data. The analysis of data based on multivariate spatial signs and ranks proceeds very much as does a traditional multivariate analysis relying on the assumption of multivariate normality; the regular L2 norm is just replaced by different L1 norms, observation vectors are replaced by spatial signs and ranks, and so on. A unified methodology starting with the simple one-sample multivariate location problem and proceeding to the general multivariate multiple linear regression case is presented. Companion estimates and tests for scatter matrices are considered as well. The R package MNM is available for c...
An Introduction to Copulas
Copulas are functions that join multivariate distribution functions to their one-dimensional margins. The study of copulas and their role in statistics is a new but vigorously growing field. In this book the student or practitioner of statistics and probability will find discussions of the fundamental properties of copulas and some of their primary applications. The applications include the study of dependence and measures of association, and the construction of families of bivariate distributions. With nearly a hundred examples and over 150 exercises, this book is suitable as a text or for self-study. The only prerequisite is an upper level undergraduate course in probability and mathematical statistics, although some familiarity with nonparametric statistics would be useful. Knowledge of measure-theoretic probability is not required. Roger B. Nelsen is Professor of Mathematics at Lewis & Clark College in Portland, Oregon. He is also the author of "Proofs Without Words: Exercises in Visual Thinking," published by the Mathematical Association of America.
Dependence in Probability and Statistics
This account of recent works on weakly dependent, long memory and multifractal processes introduces new dependence measures for studying complex stochastic systems and includes other topics such as the dependence structure of max-stable processes.
Computation of Multivariate Normal and t Probabilities
Multivariate normal and t probabilities are needed for statistical inference in many applications. Modern statistical computation packages provide functions for the computation of these probabilities for problems with one or two variables. This book describes recently developed methods for accurate and efficient computation of the required probability values for problems with two or more variables. The book discusses methods for specialized problems as well as methods for general problems. The book includes examples that illustrate the probability computations for a variety of applications.
