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Author : Kung-Sik Chan,Howell Tong
Publisher : Unknown
Page : 324 pages
File Size : 50,6 Mb
Release : 2014-01-15
Category : Electronic
ISBN : 1475734654
Author : Kung-Sik Chan,Howell Tong
Publisher : Springer Science & Business Media
Page : 312 pages
File Size : 43,6 Mb
Release : 2013-03-09
Category : Mathematics
ISBN : 9781475734645
This book discusses dynamical systems that are typically driven by stochastic dynamic noise. It is written by two statisticians essentially for the statistically inclined readers. It covers many of the contributions made by the statisticians in the past twenty years or so towards our understanding of estimation, the Lyapunov-like index, the nonparametric regression, and many others, many of which are motivated by their dynamical system counterparts but have now acquired a distinct statistical flavor.
Author : Richard A. Berk
Publisher : Springer Science & Business Media
Page : 373 pages
File Size : 52,6 Mb
Release : 2008-06-14
Category : Mathematics
ISBN : 9780387775012
Statistical Learning from a Regression Perspective considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this is can be seen as an extension of nonparametric regression. Among the statistical learning procedures examined are bagging, random forests, boosting, and support vector machines. Response variables may be quantitative or categorical. Real applications are emphasized, especially those with practical implications. One important theme is the need to explicitly take into account asymmetric costs in the fitting process. For example, in some situations false positives may be far less costly than false negatives. Another important theme is to not automatically cede modeling decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Yet another important theme is to appreciate the limitation of one’s data and not apply statistical learning procedures that require more than the data can provide. The material is written for graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R.
Author : J L Jensen,Wilfrid S. Kendall
Publisher : CRC Press
Page : 324 pages
File Size : 54,5 Mb
Release : 1993-07-22
Category : Mathematics
ISBN : 0412465302
This volume consists of a collection of tutorial papers by leading experts on statistical and probabilistic aspects of chaos and networks, in particular neural networks. While written for the non-expert, they are intended to bring the reader up to the forefront of knowledge and research in the subject areas concerned. The papers, which contain extensive references to the literature, can separately or in various combinations serve as bases for short- or full-length courses, at graduate or more advanced levels. The papers are directed not only to mathematical statisticians but also to students and researchers in related fields of biology, engineering, geology, physics and probability.
Author : Sadanori Konishi,Genshiro Kitagawa
Publisher : Springer Science & Business Media
Page : 282 pages
File Size : 50,6 Mb
Release : 2008
Category : Business & Economics
ISBN : 9780387718866
Statistical modeling is a critical tool in scientific research. This book provides comprehensive explanations of the concepts and philosophy of statistical modeling, together with a wide range of practical and numerical examples. The authors expect this work to be of great value not just to statisticians but also to researchers and practitioners in various fields of research such as information science, computer science, engineering, bioinformatics, economics, marketing and environmental science. It’s a crucial area of study, as statistical models are used to understand phenomena with uncertainty and to determine the structure of complex systems. They’re also used to control such systems, as well as to make reliable predictions in various natural and social science fields.
Author : F. Liese,Klaus-J. Miescke
Publisher : Springer Science & Business Media
Page : 696 pages
File Size : 46,7 Mb
Release : 2008-12-30
Category : Mathematics
ISBN : 9780387731940
For advanced graduate students, this book is a one-stop shop that presents the main ideas of decision theory in an organized, balanced, and mathematically rigorous manner, while observing statistical relevance. All of the major topics are introduced at an elementary level, then developed incrementally to higher levels. The book is self-contained as it provides full proofs, worked-out examples, and problems. The authors present a rigorous account of the concepts and a broad treatment of the major results of classical finite sample size decision theory and modern asymptotic decision theory. With its broad coverage of decision theory, this book fills the gap between standard graduate texts in mathematical statistics and advanced monographs on modern asymptotic theory.
Author : Enrico Bernardi,Silvia Romagnoli
Publisher : Springer Nature
Page : 206 pages
File Size : 43,6 Mb
Release : 2021-03-22
Category : Business & Economics
ISBN : 9783030642501
This book on counting statistics presents a novel copula-based approach to counting dependent random events. It combines clustering, combinatorics-based algorithms and dependence structure in order to tackle and simplify complex problems, without disregarding the hierarchy of or interconnections between the relevant variables. These problems typically arise in real-world applications and computations involving big data in finance, insurance and banking, where experts are confronted with counting variables in monitoring random events. In this new approach, combinatorial distributions of random events are the core element. In order to deal with the high-dimensional features of the problem, the combinatorial techniques are used together with a clustering approach, where groups of variables sharing common characteristics and similarities are identified and the dependence structure within groups is taken into account. The original problems can then be modeled using new classes of copulas, referred to here as clusterized copulas, which are essentially based on preliminary groupings of variables depending on suitable characteristics and hierarchical aspects. The book includes examples and real-world data applications, with a special focus on financial applications, where the new algorithms’ performance is compared to alternative approaches and further analyzed. Given its scope, the book will be of interest to master students, PhD students and researchers whose work involves or can benefit from the innovative methodologies put forward here. It will also stimulate the empirical use of new approaches among professionals and practitioners in finance, insurance and banking.
Author : Yury A. Kutoyants
Publisher : Springer Science & Business Media
Page : 493 pages
File Size : 42,9 Mb
Release : 2013-03-09
Category : Mathematics
ISBN : 9781447138662
The first book in inference for stochastic processes from a statistical, rather than a probabilistic, perspective. It provides a systematic exposition of theoretical results from over ten years of mathematical literature and presents, for the first time in book form, many new techniques and approaches.
Author : Trevor Hastie,Robert Tibshirani,Jerome Friedman
Publisher : Springer Science & Business Media
Page : 545 pages
File Size : 53,8 Mb
Release : 2013-11-11
Category : Mathematics
ISBN : 9780387216065
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
Author : Jian-Xin Pan,Kai-Tai Fang
Publisher : Springer Science & Business Media
Page : 406 pages
File Size : 43,5 Mb
Release : 2012-11-06
Category : Mathematics
ISBN : 9780387218120
This book systematically introduces the theory of the GCM with particular emphasis on their multivariate statistical diagnostics, which are based mainly on recent developments made by the authors and their collaborators. Provided are complete proofs of theorems as well as practical data sets and MATLAB code.
Author : Nhu D. Le,James V. Zidek
Publisher : Springer Science & Business Media
Page : 338 pages
File Size : 51,8 Mb
Release : 2006-09-13
Category : Science
ISBN : 9780387354293
This book provides a broad introduction to the subject of environmental space-time processes, addressing the role of uncertainty. It covers a spectrum of technical matters from measurement to environmental epidemiology to risk assessment. It showcases non-stationary vector-valued processes, while treating stationarity as a special case. In particular, with members of their research group the authors developed within a hierarchical Bayesian framework, the new statistical approaches presented in the book for analyzing, modeling, and monitoring environmental spatio-temporal processes. Furthermore they indicate new directions for development.
Author : James O. Berger
Publisher : Springer Science & Business Media
Page : 633 pages
File Size : 54,6 Mb
Release : 2013-03-14
Category : Mathematics
ISBN : 9781475742862
In this new edition the author has added substantial material on Bayesian analysis, including lengthy new sections on such important topics as empirical and hierarchical Bayes analysis, Bayesian calculation, Bayesian communication, and group decision making. With these changes, the book can be used as a self-contained introduction to Bayesian analysis. In addition, much of the decision-theoretic portion of the text was updated, including new sections covering such modern topics as minimax multivariate (Stein) estimation.
Author : Samaradasa Weerahandi
Publisher : Springer Science & Business Media
Page : 343 pages
File Size : 51,7 Mb
Release : 2013-12-01
Category : Mathematics
ISBN : 9781461208259
Now available in paperback, this book covers some recent developments in statistical inference. It provides methods applicable in problems involving nuisance parameters such as those encountered in comparing two exponential distributions or in ANOVA without the assumption of equal error variances. The generalized procedures are shown to be more powerful in detecting significant experimental results and in avoiding misleading conclusions.
Author : Marco A.R. Ferreira,Herbert K.H. Lee
Publisher : Springer Science & Business Media
Page : 243 pages
File Size : 48,5 Mb
Release : 2007-07-27
Category : Business & Economics
ISBN : 9780387708973
This highly useful book contains methodology for the analysis of data that arise from multiscale processes. It brings together a number of recent developments and makes them accessible to a wider audience. Taking a Bayesian approach allows for full accounting of uncertainty, and also addresses the delicate issue of uncertainty at multiple scales. These methods can handle different amounts of prior knowledge at different scales, as often occurs in practice.
Author : Joseph Glaz,Joseph Naus,Sylvan Wallenstein
Publisher : Springer Science & Business Media
Page : 380 pages
File Size : 55,9 Mb
Release : 2013-03-09
Category : Mathematics
ISBN : 9781475734607
In many statistical applications, scientists have to analyze the occurrence of observed clusters of events in time or space. Scientists are especially interested in determining whether an observed cluster of events has occurred by chance if it is assumed that the events are distributed independently and uniformly over time or space. Scan statistics have relevant applications in many areas of science and technology including geology, geography, medicine, minefield detection, molecular biology, photography, quality control and reliability theory and radio-optics.