Statistical Methods And Problems In Infinite Dimensional Spaces
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| Author | : Evarist Giné |
| Publisher | : Cambridge University Press |
| Total Pages | : 706 |
| Release | : 2021-03-25 |
| Genre | : Mathematics |
| ISBN | : 1009022784 |
In nonparametric and high-dimensional statistical models, the classical Gauss–Fisher–Le Cam theory of the optimality of maximum likelihood estimators and Bayesian posterior inference does not apply, and new foundations and ideas have been developed in the past several decades. This book gives a coherent account of the statistical theory in infinite-dimensional parameter spaces. The mathematical foundations include self-contained 'mini-courses' on the theory of Gaussian and empirical processes, approximation and wavelet theory, and the basic theory of function spaces. The theory of statistical inference in such models - hypothesis testing, estimation and confidence sets - is presented within the minimax paradigm of decision theory. This includes the basic theory of convolution kernel and projection estimation, but also Bayesian nonparametrics and nonparametric maximum likelihood estimation. In a final chapter the theory of adaptive inference in nonparametric models is developed, including Lepski's method, wavelet thresholding, and adaptive inference for self-similar functions. Winner of the 2017 PROSE Award for Mathematics.
| Author | : Geoffrey de Villiers |
| Publisher | : CRC Press |
| Total Pages | : 569 |
| Release | : 2016-10-03 |
| Genre | : Mathematics |
| ISBN | : 1498758126 |
"This beautiful book can be read as a novel presenting carefully our quest to get more and more information from our observations and measurements. Its authors are particularly good at relating it." --Pierre C. Sabatier "This is a unique text - a labor of love pulling together for the first time the remarkably large array of mathematical and statistical techniques used for analysis of resolution in many systems of importance today – optical, acoustical, radar, etc.... I believe it will find widespread use and value." --Dr. Robert G.W. Brown, Chief Executive Officer, American Institute of Physics "The mix of physics and mathematics is a unique feature of this book which can be basic not only for PhD students but also for researchers in the area of computational imaging." --Mario Bertero, Professor, University of Geneva "a tour-de-force covering aspects of history, mathematical theory and practical applications. The authors provide a penetrating insight into the often confused topic of resolution and in doing offer a unifying approach to the subject that is applicable not only to traditional optical systems but also modern day, computer-based systems such as radar and RF communications." --Prof. Ian Proudler, Loughborough University "a ‘must have’ for anyone interested in imaging and the spatial resolution of images. This book provides detailed and very readable account of resolution in imaging and organizes the recent history of the subject in excellent fashion.... I strongly recommend it." --Michael A. Fiddy, Professor, University of North Carolina at Charlotte This book brings together the concept of resolution, which limits what we can determine about our physical world, with the theory of linear inverse problems, emphasizing practical applications. The book focuses on methods for solving illposed problems that do not have unique stable solutions. After introducing basic concepts, the contents address problems with "continuous" data in detail before turning to cases of discrete data sets. As one of the unifying principles of the text, the authors explain how non-uniqueness is a feature of measurement problems in science where precision and resolution is essentially always limited by some kind of noise.
| Author | : Jeremy J. Becnel |
| Publisher | : CRC Press |
| Total Pages | : 266 |
| Release | : 2020-12-21 |
| Genre | : Mathematics |
| ISBN | : 1000328287 |
Over the past six decades, several extremely important fields in mathematics have been developed. Among these are Itô calculus, Gaussian measures on Banach spaces, Malliavan calculus, and white noise distribution theory. These subjects have many applications, ranging from finance and economics to physics and biology. Unfortunately, the background information required to conduct research in these subjects presents a tremendous roadblock. The background material primarily stems from an abstract subject known as infinite dimensional topological vector spaces. While this information forms the backdrop for these subjects, the books and papers written about topological vector spaces were never truly written for researchers studying infinite dimensional analysis. Thus, the literature for topological vector spaces is dense and difficult to digest, much of it being written prior to the 1960s. Tools for Infinite Dimensional Analysis aims to address these problems by providing an introduction to the background material for infinite dimensional analysis that is friendly in style and accessible to graduate students and researchers studying the above-mentioned subjects. It will save current and future researchers countless hours and promote research in these areas by removing an obstacle in the path to beginning study in areas of infinite dimensional analysis. Features Focused approach to the subject matter Suitable for graduate students as well as researchers Detailed proofs of primary results
| Author | : Hector O. Fattorini |
| Publisher | : Cambridge University Press |
| Total Pages | : 828 |
| Release | : 1999-03-28 |
| Genre | : Computers |
| ISBN | : 9780521451253 |
Treats optimal problems for systems described by ODEs and PDEs, using an approach that unifies finite and infinite dimensional nonlinear programming.
| Author | : René Carmona |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 236 |
| Release | : 2007-05-22 |
| Genre | : Mathematics |
| ISBN | : 3540270671 |
This book presents the mathematical issues that arise in modeling the interest rate term structure by casting the interest-rate models as stochastic evolution equations in infinite dimensions. The text includes a crash course on interest rates, a self-contained introduction to infinite dimensional stochastic analysis, and recent results in interest rate theory. From the reviews: "A wonderful book. The authors present some cutting-edge math." --WWW.RISKBOOK.COM
| Author | : Enea G. Bongiorno |
| Publisher | : Società Editrice Esculapio |
| Total Pages | : 300 |
| Release | : 2014-05-21 |
| Genre | : Mathematics |
| ISBN | : 8874887639 |
The interest towards Functional and Operatorial Statistics, and, more in general, towards infinite-dimensional statistics has dramatically increased in the statistical community and in many other applied scientific areas where people faces functional data. This volume collects the works selected and presented at the Third Edition of the International Workshop on Functional and Operatorial Statistics held in Stresa, Italy, from the 19th to the 21st of June 2014 (IWFOS’2014). The meeting represents an opportunity of bringing together leading researchers active on these topics both for what concerns theoretical aspects and a wide range of applications in various fields. To promote collaborations with other important strictly related areas of infinite-dimensional Statistics, such as High Dimensional Statistics and Model Selection Procedures, this book hosts works in the latter research subjects too.
| Author | : Victor M. Panaretos |
| Publisher | : Springer Nature |
| Total Pages | : 157 |
| Release | : 2020-03-10 |
| Genre | : Mathematics |
| ISBN | : 3030384381 |
This open access book presents the key aspects of statistics in Wasserstein spaces, i.e. statistics in the space of probability measures when endowed with the geometry of optimal transportation. Further to reviewing state-of-the-art aspects, it also provides an accessible introduction to the fundamentals of this current topic, as well as an overview that will serve as an invitation and catalyst for further research. Statistics in Wasserstein spaces represents an emerging topic in mathematical statistics, situated at the interface between functional data analysis (where the data are functions, thus lying in infinite dimensional Hilbert space) and non-Euclidean statistics (where the data satisfy nonlinear constraints, thus lying on non-Euclidean manifolds). The Wasserstein space provides the natural mathematical formalism to describe data collections that are best modeled as random measures on Euclidean space (e.g. images and point processes). Such random measures carry the infinite dimensional traits of functional data, but are intrinsically nonlinear due to positivity and integrability restrictions. Indeed, their dominating statistical variation arises through random deformations of an underlying template, a theme that is pursued in depth in this monograph.
| Author | : B. Grigelionis |
| Publisher | : Walter de Gruyter GmbH & Co KG |
| Total Pages | : 656 |
| Release | : 2020-05-18 |
| Genre | : Mathematics |
| ISBN | : 3112314190 |
No detailed description available for "GRIGELIONIS: PROCEEDINGS OF THE FIFTH VILNIUS CONFERE E-BOOK".
| Author | : Yasuo Yamasaki |
| Publisher | : World Scientific |
| Total Pages | : 276 |
| Release | : 1985 |
| Genre | : Science |
| ISBN | : 9789971978525 |
This book is based on lectures given at Yale and Kyoto Universities and provides a self-contained detailed exposition of the following subjects: 1) The construction of infinite dimensional measures, 2) Invariance and quasi-invariance of measures under translations. This book furnishes an important tool for the analysis of physical systems with infinite degrees of freedom (such as field theory, statistical physics and field dynamics) by providing material on the foundations of these problems.
| Author | : Luis Tenorio |
| Publisher | : SIAM |
| Total Pages | : 275 |
| Release | : 2017-07-06 |
| Genre | : Mathematics |
| ISBN | : 1611974917 |
Inverse problems are found in many applications, such as medical imaging, engineering, astronomy, and geophysics, among others. To solve an inverse problem is to recover an object from noisy, usually indirect observations. Solutions to inverse problems are subject to many potential sources of error introduced by approximate mathematical models, regularization methods, numerical approximations for efficient computations, noisy data, and limitations in the number of observations; thus it is important to include an assessment of the uncertainties as part of the solution. Such assessment is interdisciplinary by nature, as it requires, in addition to knowledge of the particular application, methods from applied mathematics, probability, and statistics. This book bridges applied mathematics and statistics by providing a basic introduction to probability and statistics for uncertainty quantification in the context of inverse problems, as well as an introduction to statistical regularization of inverse problems. The author covers basic statistical inference, introduces the framework of ill-posed inverse problems, and explains statistical questions that arise in their applications. An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems?includes many examples that explain techniques which are useful to address general problems arising in uncertainty quantification, Bayesian and non-Bayesian statistical methods and discussions of their complementary roles, and analysis of a real data set to illustrate the methodology covered throughout the book.
