all of statistics a concise course in statistical inference springer texts in statistics

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All Of Statistics

Author : Larry Wasserman
ISBN : 9780387217369
Genre : Mathematics
File Size : 60. 11 MB
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Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.

Vorlesungen Ber Mathematische Statistik

Author : Helmut Pruscha
ISBN : 9783322829665
Genre : Mathematics
File Size : 82. 71 MB
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Das Buch stellt wichtige Konzepte und Ergebnisse der Mathematischen Statistik vor. Auf klassische Schätz- und Testtheorie und das lineare Modell der Statistik wird ebenso eingegangen wie auf Glättungs- und Resamplingmethoden und nichtlineare und nichtparametrische Modelle. Das Studium des asymptotischen statistischen Verhaltens zeigt zudem die Qualität der Verfahren bei wachsendem Stichprobenumfang auf und ermöglicht die Konstruktion von Tests und Konfidenzintervallen.

Statistisches Methodenbuch

Author : Helmut Pruscha
ISBN : 9783540293057
Genre : Mathematics
File Size : 87. 79 MB
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Statistik dringt in immer weitere Bereiche der Naturwissenschaften, der Technik, Medizin und Ökologie vor. Den immer komplexer werdenden Daten muss der Statistiker mit einem ausreichenden Methodenarsenal und mit entsprechender statistischer Software gerecht werden. Ausgehend von Grundbegriffen und elementaren Verfahren wendet sich das Buch zunächst den Standardverfahren der multiplen Regression und den Modellen der Varianzanalyse zu. Nichtlineare statistische Methoden, wie sie namentlich bei kategoriellen Daten benötigt werden, und nichtparametrische Methoden zur Kurvenanpassung folgen. Besteht die Zielgrösse aus mehreren korrelierten Variablen gleichzeitig, so gelangt man zu den multivariaten Verfahren, die eine ausserordentliche Attraktivität in den 'life sciences' erlangt haben: Manova, Diskriminanz-, Faktor- und Clusteranalyse. Das Buch schließt mit den Analysemethoden für Zeitreihen, die sowohl in der Ökologie als auch in der Ökonomie wachsende Bedeutung gewinnen. Jedes Verfahren wird durch Anwendungsbeispiele illustriert, die grösseren real-wissenschaftlichen Fallstudien entnommen sind, sowie durch Programmcodes ergänzt, die in der Syntax der bekannten Statistikpakete Splus/R, SPSS oder SAS geschrieben sind. Awendern in den oben genannten Gebieten, die mit komplexeren Auswertungsproblemen konfrontiert sind, und die bis zur Feinanalyse ihrer Daten vordringen wollen wie Studenten und Dozenten in den Methodenwissenschaften (Mathematik, Statistik, Informatik) erhalten einen Einblick in die Fragestellungen und in die Lösungsangebote der Statistik und der sie unterstützenden Programmpakete.

All Of Nonparametric Statistics

Author : Larry Wasserman
ISBN : 0387306234
Genre : Mathematics
File Size : 41. 96 MB
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This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at Masters or PhD level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book’s dual approach includes a mixture of methodology and theory.

Statistical Regression And Classification

Author : Norman Matloff
ISBN : 9781351645898
Genre : Business & Economics
File Size : 90. 75 MB
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Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough treatment of classical linear and generalized linear models, supplemented with introductory material on machine learning methods. * Since classification is the focus of many contemporary applications, the book covers this topic in detail, especially the multiclass case. * In view of the voluminous nature of many modern datasets, there is a chapter on Big Data. * Has special Mathematical and Computational Complements sections at ends of chapters, and exercises are partitioned into Data, Math and Complements problems. * Instructors can tailor coverage for specific audiences such as majors in Statistics, Computer Science, or Economics. * More than 75 examples using real data. The book treats classical regression methods in an innovative, contemporary manner. Though some statistical learning methods are introduced, the primary methodology used is linear and generalized linear parametric models, covering both the Description and Prediction goals of regression methods. The author is just as interested in Description applications of regression, such as measuring the gender wage gap in Silicon Valley, as in forecasting tomorrow's demand for bike rentals. An entire chapter is devoted to measuring such effects, including discussion of Simpson's Paradox, multiple inference, and causation issues. Similarly, there is an entire chapter of parametric model fit, making use of both residual analysis and assessment via nonparametric analysis. Norman Matloff is a professor of computer science at the University of California, Davis, and was a founder of the Statistics Department at that institution. His current research focus is on recommender systems, and applications of regression methods to small area estimation and bias reduction in observational studies. He is on the editorial boards of the Journal of Statistical Computation and the R Journal. An award-winning teacher, he is the author of The Art of R Programming and Parallel Computation in Data Science: With Examples in R, C++ and CUDA.

Applied Cryptography And Network Security

Author : Michel Abdalla
ISBN : 9783642019562
Genre : Computers
File Size : 47. 40 MB
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This book constitutes the refereed proceedings of the 7th International Conference on Applied Cryptography and Network Security, ACNS 2009, held in Paris-Rocquencourt, France, in June 2009. The 32 revised full papers presented were carefully reviewed and selected from 150 submissions. The papers are organized in topical sections on key exchange, secure computation, public-key encryption, network security, traitor tracing, authentication and anonymity, hash fundtions, lattices, and side-channel attacks.

Cryptographic Hardware And Embedded Systems Ches 2009

Author : Christophe Clavier
ISBN : 9783642041372
Genre : Computers
File Size : 66. 99 MB
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This book constitutes the refereed proceedings of the 11th International Workshop on Cryptographic Hardware and Embedded Systems, CHES 2009, held in Lausanne, Switzerland during September 6-9, 2009. The book contains 3 invited talks and 29 revised full papers which were carefully reviewed and selected from 148 submissions. The papers are organized in topical sections on software implementations, side channel analysis of secret key cryptosystems, side channel analysis of public key cryptosystems, side channel and fault analysis countermeasures, pairing-based cryptography, new ciphers and efficient implementations, TRNGs and device identification, hardware trojans and trusted ICs, theoretical aspects, and fault analysis.

Mathematical Analysis Of Urban Spatial Networks

Author : Philippe Blanchard
ISBN : 9783540878292
Genre : Science
File Size : 75. 1 MB
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Cities can be considered to be among the largest and most complex artificial networks created by human beings. Due to the numerous and diverse human-driven activities, urban network topology and dynamics can differ quite substantially from that of natural networks and so call for an alternative method of analysis. The intent of the present monograph is to lay down the theoretical foundations for studying the topology of compact urban patterns, using methods from spectral graph theory and statistical physics. These methods are demonstrated as tools to investigate the structure of a number of real cities with widely differing properties: medieval German cities, the webs of city canals in Amsterdam and Venice, and a modern urban structure such as found in Manhattan. Last but not least, the book concludes by providing a brief overview of possible applications that will eventually lead to a useful body of knowledge for architects, urban planners and civil engineers.

Introduction To Semi Supervised Learning

Author : Xiaojin Zhu
ISBN : 9781598295474
Genre : Computers
File Size : 70. 13 MB
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Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical Machine Learning / Overview of Semi-Supervised Learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised Learning / Semi-Supervised Support Vector Machines / Human Semi-Supervised Learning / Theory and Outlook

Genetic Programming

Author : Leonardo Vanneschi
ISBN : 9783642011801
Genre : Computers
File Size : 65. 39 MB
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This book constitutes the refereed proceedings of the 11th European Conference on Genetic Programming, EuroGP 2009, held in Tübingen, Germany, in April 2009 colocated with the Evo* 2009 events. The 21 revised plenary papers and 9 revised poster papers were carefully reviewed and selected from a total of 57 submissions. A great variety of topics are presented reflecting the current state of research in the field of genetic programming, including the latest work on representations, theory, operators and analysis, feature selection, generalisation, coevolution and numerous applications.

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