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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.
Parametric and Nonparametric Inference from Record-Breaking Data
By providing a comprehensive look at statistical inference from record-breaking data in both parametric and nonparametric settings, this book treats the area of nonparametric function estimation from such data in detail. Its main purpose is to fill this void on general inference from record values. Statisticians, mathematicians, and engineers will find the book useful as a research reference. It can also serve as part of a graduate-level statistics or mathematics course.
Spatial Statistics: Methodological Aspects and Applications
This volume contains presentations by eminent researchers: Statistical Inference for Spatial Processes; Image Analysis; Applications of Spatial Statistics in Earth, Environmental, and Health Sciences; and Statistics of Brain Mapping. They range from asymptotic considerations for spatial processes to practical considerations related to particular applications including important methodological aspects. Many contributions concern image analysis, mainly images related to brain mapping.
Rasch Models
Thirty-two years after the publication of the legendary 'Rasch book' (Rasch, 1960), the rich literature on the Rasch model and its extensions was scattered in journals and many less accessible sources, including 'grey' literature. When asked by students or junior researchers for references to the Rasch model, it was a typical reaction on the part of the editors to state that it was difficult to name one, or just a few; actually, only a whole list of references differing in notation and level of formal abstraction seemed to meet the request in most cases. Therefore, in 1992 the editors decided to invite a number of outstanding authors in the field of Rasch modeling to contribute to a book pre...
International Corporate Brand Management
Marketers have to understand how the information that consumers associate with a company and its products affects their responses to those products. Adressing this issue, Markus Meierer analyzes firstly if consumers from Germany, France, Romania, Russia, and the USA perceive an internationally standardized corporate brand homogenously as well as if a positive effect on consumers' product response exists. Secondly he investigates if consumers perceive corporate and product brand as reciprocally related across countries as well as how the direct and indirect effects of corporate and product branding on consumers' product response look like.
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Weak Dependence: With Examples and Applications
This book develops Doukhan/Louhichi's 1999 idea to measure asymptotic independence of a random process. The authors, who helped develop this theory, propose examples of models fitting such conditions: stable Markov chains, dynamical systems or more complicated models, nonlinear, non-Markovian, and heteroskedastic models with infinite memory. Applications are still needed to develop a method of analysis for nonlinear times series, and this book provides a strong basis for additional studies.
Noise Reduction by Wavelet Thresholding
Wavelet methods have become a widely spread tool in signal and image process ing tasks. This book deals with statistical applications, especially wavelet based smoothing. The methods described in this text are examples of non-linear and non parametric curve fitting. The book aims to contribute to the field both among statis ticians and in the application oriented world (including but not limited to signals and images). Although it also contains extensive analyses of some existing methods, it has no intention whatsoever to be a complete overview of the field: the text would show too much bias towards my own algorithms. I rather present new material and own insights in the questions involved w...
Linear Regression
The book covers the basic theory of linear regression models and presents a comprehensive survey of different estimation techniques as alternatives and complements to least squares estimation. Proofs are given for the most relevant results, and the presented methods are illustrated with the help of numerical examples and graphics. Special emphasis is placed on practicability and possible applications. The book is rounded off by an introduction to the basics of decision theory and an appendix on matrix algebra.
