Reproducing Kernel Hilbert Spaces in Probability and Statistics


Author: Alain Berlinet,Christine Thomas-Agnan
Publisher: Springer Science & Business Media
ISBN: 1441990968
Category: Business & Economics
Page: 355
View: 8701

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The book covers theoretical questions including the latest extension of the formalism, and computational issues and focuses on some of the more fruitful and promising applications, including statistical signal processing, nonparametric curve estimation, random measures, limit theorems, learning theory and some applications at the fringe between Statistics and Approximation Theory. It is geared to graduate students in Statistics, Mathematics or Engineering, or to scientists with an equivalent level.

An Introduction to the Theory of Reproducing Kernel Hilbert Spaces


Author: Vern I. Paulsen,Mrinal Raghupathi
Publisher: Cambridge University Press
ISBN: 1316558738
Category: Mathematics
Page: N.A
View: 5722

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Reproducing kernel Hilbert spaces have developed into an important tool in many areas, especially statistics and machine learning, and they play a valuable role in complex analysis, probability, group representation theory, and the theory of integral operators. This unique text offers a unified overview of the topic, providing detailed examples of applications, as well as covering the fundamental underlying theory, including chapters on interpolation and approximation, Cholesky and Schur operations on kernels, and vector-valued spaces. Self-contained and accessibly written, with exercises at the end of each chapter, this unrivalled treatment of the topic serves as an ideal introduction for graduate students across mathematics, computer science, and engineering, as well as a useful reference for researchers working in functional analysis or its applications.

Verallgemeinerte stochastische Prozesse

Modellierung und Anwendung technischer Rauschprozesse
Author: Stefan Schäffler
Publisher: Springer-Verlag
ISBN: 366254265X
Category: Mathematics
Page: 183
View: 8673

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Dieses Lehrbuch behandelt die in Natur- und Ingenieurwissenschaften eine zentrale Rolle spielenden Rauschprozesse, wie weißes Rauschen in der Raumsondenkommunikation oder thermisches Rauschen und Schrotrauschen in elektronischen Bauelementen.In dieser Form einzigartig, entwickelt der Autor die mathematische Theorie der verallgemeinerten stochastischen Prozesse und spricht dabei die Anwendung dieser mathematischen Objekte in der Praxis (z.B. Schaltkreissimulation, digitale Nachrichtenübertragung und Bildverarbeitung) an; somit dient dieses Lehrbuch auch als praxisrelevante Einführung in die Modellierung und Verwendung technischer Rauschprozesse. Die mathematische Modellierung von Rauschprozessen führt auf die Theorie stochastischer Prozesse auf Basis verallgemeinerter Funktionen (Distributionen), ohne die kein Handy funktionieren und Anwendungen wie die Simulation komplexer elektronischer Schaltungen unmöglich wäre.Für Anwender und interessierte Mathematiker bietet dieses Werk erstmals einen mathematisch fundierten Einblick in diese Thematik.

Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators


Author: Tailen Hsing,Randall Eubank
Publisher: John Wiley & Sons
ISBN: 0470016914
Category: Mathematics
Page: 480
View: 7781

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Functional data is data in the form of curves that is becoming a popular method for interpreting scientific data. Statistical Analysis of Functional Data provides an authoritative account of function data analysis covering its foundations, theory, methodology, and practical implementation. It also contains examples taken from a wide range of disciplines, including finance, medicine, and psychology. The book includes a supporting Web site hosting the real data sets analyzed in the book and related software. Statistical researchers or practitioners analyzing functional data will find this book useful.

Hilbert Space Methods in Probability and Statistical Inference


Author: Christopher G. Small,Don L. McLeish
Publisher: John Wiley & Sons
ISBN: 1118165535
Category: Mathematics
Page: 270
View: 4869

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Explains how Hilbert space techniques cross the boundaries into the foundations of probability and statistics. Focuses on the theory of martingales stochastic integration, interpolation and density estimation. Includes a copious amount of problems and examples.

Probability for Statistics and Machine Learning

Fundamentals and Advanced Topics
Author: Anirban DasGupta
Publisher: Springer Science & Business Media
ISBN: 9781441996343
Category: Mathematics
Page: 784
View: 5535

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This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It is written in an extremely accessible style, with elaborate motivating discussions and numerous worked out examples and exercises. The book has 20 chapters on a wide range of topics, 423 worked out examples, and 808 exercises. It is unique in its unification of probability and statistics, its coverage and its superb exercise sets, detailed bibliography, and in its substantive treatment of many topics of current importance. This book can be used as a text for a year long graduate course in statistics, computer science, or mathematics, for self-study, and as an invaluable research reference on probabiliity and its applications. Particularly worth mentioning are the treatments of distribution theory, asymptotics, simulation and Markov Chain Monte Carlo, Markov chains and martingales, Gaussian processes, VC theory, probability metrics, large deviations, bootstrap, the EM algorithm, confidence intervals, maximum likelihood and Bayes estimates, exponential families, kernels, and Hilbert spaces, and a self contained complete review of univariate probability.

Stochastics in Finite and Infinite Dimensions

In Honor of Gopinath Kallianpur
Author: Takeyuki Hida,Rajeeva L. Karandikar,Hiroshi Kunita,Balram S. Rajput,Shinzo Watanabe,Jie Xiong
Publisher: Springer Science & Business Media
ISBN: 1461201675
Category: Mathematics
Page: 411
View: 8241

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During the last fifty years, Gopinath Kallianpur has made extensive and significant contributions to diverse areas of probability and statistics, including stochastic finance, Fisher consistent estimation, non-linear prediction and filtering problems, zero-one laws for Gaussian processes and reproducing kernel Hilbert space theory, and stochastic differential equations in infinite dimensions. To honor Kallianpur's pioneering work and scholarly achievements, a number of leading experts have written research articles highlighting progress and new directions of research in these and related areas. This commemorative volume, dedicated to Kallianpur on the occasion of his seventy-fifth birthday, will pay tribute to his multi-faceted achievements and to the deep insight and inspiration he has so graciously offered his students and colleagues throughout his career. Contributors to the volume: S. Aida, N. Asai, K. B. Athreya, R. N. Bhattacharya, A. Budhiraja, P. S. Chakraborty, P. Del Moral, R. Elliott, L. Gawarecki, D. Goswami, Y. Hu, J. Jacod, G. W. Johnson, L. Johnson, T. Koski, N. V. Krylov, I. Kubo, H.-H. Kuo, T. G. Kurtz, H. J. Kushner, V. Mandrekar, B. Margolius, R. Mikulevicius, I. Mitoma, H. Nagai, Y. Ogura, K. R. Parthasarathy, V. Perez-Abreu, E. Platen, B. V. Rao, B. Rozovskii, I. Shigekawa, K. B. Sinha, P. Sundar, M. Tomisaki, M. Tsuchiya, C. Tudor, W. A. Woycynski, J. Xiong.

Maß und Wahrscheinlichkeit


Author: Klaus D. Schmidt
Publisher: Springer-Verlag
ISBN: 3642210260
Category: Mathematics
Page: 484
View: 675

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Die Wahrscheinlichkeitstheorie hat durch neue Anwendungen in der Wirtschaft auch in der Lehre an Bedeutung gewonnen. Sie beruht auf der Maß- und Integrationstheorie – gleichzeitig eine der Grundlagen der Funktionalanalysis. Das Buch bietet eine Einführung in die Wahrscheinlichkeitstheorie. Dabei wird die systematische Darstellung der klassischen Themen durch Beispiele und Aufgaben ergänzt, so dass die Theorie, aber auch Anwendungen vertieft werden können. Der Stoff wurde so aufbereitet, dass er ohne Vorkenntnisse erarbeitet werden kann.

Foundations of Estimation Theory


Author: L. Kubacek
Publisher: Elsevier
ISBN: 0444598081
Category: Mathematics
Page: 335
View: 8282

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The application of estimation theory renders the processing of experimental results both rational and effective, and thus helps not only to make our knowledge more precise but to determine the measure of its reliability. As a consequence, estimation theory is indispensable in the analysis of the measuring processes and of experiments in general. The knowledge necessary for studying this book encompasses the disciplines of probability and mathematical statistics as studied in the third or fourth year at university. For readers interested in applications, comparatively detailed chapters on linear and quadratic estimations, and normality of observation vectors have been included. Chapter 2 includes selected items of information from algebra, functional analysis and the theory of probability, intended to facilitate the reading of the text proper and to save the reader looking up individual theorems in various textbooks and papers; it is mainly devoted to the reproducing kernel Hilbert spaces, helpful in solving many estimation problems. The text proper of the book begins with Chapter 3. This is divided into two parts: the first deals with sufficient statistics, complete sufficient statistics, minimal sufficient statistics and relations between them; the second contains the mostimportant inequalities of estimation theory for scalar and vector valued parameters and presents properties of the exponential family of distributions. The fourth chapter is an introduction to asymptotic methods of estimation. The method of statistical moments and the maximum-likelihood method are investigated. The sufficient conditions for asymptotical normality of the estimators are given for both methods. The linear and quadratic methods of estimation are dealt with in the fifth chapter. The method of least squares estimation is treated. Five basic regular versions of the regression model and the unified linear model of estimation are described. Unbiased estimators for unit dispersion (factor of the covariance matrix) are given for all mentioned cases. The equivalence of the least-squares method to the method of generalized minimum norm inversion of the design matrix of the regression model is studied in detail. The problem of estimating the covariance components in the mixed model is mentioned as well. Statistical properties of linear and quadratic estimators developed in the fifth chapter in the case of normally distributed errors of measurement are given in Chapter 6. Further, the application of tensor products of Hilbert spaces generated by the covariance matrix of random error vector of observations is demonstrated. Chapter 7 reviews some further important methods of estimation theory. In the first part Wald's method of decision functions is applied to the construction of estimators. The method of contracted estimators and the method of Hoerl and Kennard are presented in the second part. The basic ideas of robustness and Bahadur's approach to estimation theory are presented in the third and fourth parts of this last chapter.

Mathematische Formeln für Wirtschaftswissenschaftler


Author: Bernd Luderer,Volker Nollau,Klaus Vetters
Publisher: Springer-Verlag
ISBN: 3658097914
Category: Mathematics
Page: 222
View: 3015

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Diese Formelsammlung ist gezielt auf die Bedürfnisse des Studiums der Wirtschaftswissenschaften an Universitäten und Fachhochschulen zugeschnitten. Sie enthält in komprimierter, übersichtlicher Form das wesentliche Grundwissen der Mathematik, Finanzmathematik und Statistik, das in den Lehrveranstaltungen des Grundstudiums wirtschaftswissenschaftlicher Studiengänge benötigt wird. In der vorliegenden 8., gründlich durchgesehenen Auflage wurden zahlreiche Ergänzungen vorgenommen. Diese betreffen u. a. die Kapitel zur Finanzmathematik, zur linearen Optimierung, zu Funktionen einer unabhängigen Variablen und zur linearen Algebra sowie ökonomische Anwendungen der Differentialrechnung, wie etwa Wachstumsbegriffe von Funktionen. Ferner wurde das Sachwortverzeichnis nochmals erweitert, um das Auffinden von Formeln und Begriffen zu erleichtern.

Handbook of Data Visualization


Author: Chun-houh Chen,Wolfgang Karl Härdle,Antony Unwin
Publisher: Springer Science & Business Media
ISBN: 9783540330370
Category: Computers
Page: 936
View: 5708

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Visualizing the data is an essential part of any data analysis. Modern computing developments have led to big improvements in graphic capabilities and there are many new possibilities for data displays. This book gives an overview of modern data visualization methods, both in theory and practice. It details modern graphical tools such as mosaic plots, parallel coordinate plots, and linked views. Coverage also examines graphical methodology for particular areas of statistics, for example Bayesian analysis, genomic data and cluster analysis, as well software for graphics.