Bootstrap Methods

Bootstrap Methods PDF
Author: Gerhard Dikta
Publisher: Springer
ISBN: 9783030734794
Size: 40.67 MB
Format: PDF, ePub, Mobi
Category : Mathematics
Languages : en
Pages : 256
View: 7104

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Book Description:

Bootstrap Methods

by Gerhard Dikta, Bootstrap Methods Book available in PDF, EPUB, Mobi Format. Download Bootstrap Methods books, This book provides a compact introduction to the bootstrap method. In addition to classical results on point estimation and test theory, multivariate linear regression models and generalized linear models are covered in detail. Special attention is given to the use of bootstrap procedures to perform goodness-of-fit tests to validate model or distributional assumptions. In some cases, new methods are presented here for the first time. The text is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form. Overall, R is given great importance throughout. Each chapter includes a section of exercises and, for the more mathematically inclined readers, concludes with rigorous proofs. The intended audience is graduate students who already have a prior knowledge of probability theory and mathematical statistics.







An Introduction To Bootstrap Methods With Applications To R

An Introduction to Bootstrap Methods with Applications to R PDF
Author: Michael R. Chernick
Publisher: John Wiley & Sons
ISBN: 1118625412
Size: 12.14 MB
Format: PDF, ePub, Mobi
Category : Mathematics
Languages : en
Pages : 240
View: 2174

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Book Description:

An Introduction To Bootstrap Methods With Applications To R

by Michael R. Chernick, An Introduction To Bootstrap Methods With Applications To R Book available in PDF, EPUB, Mobi Format. Download An Introduction To Bootstrap Methods With Applications To R books, A comprehensive introduction to bootstrap methods in the Rprogramming environment Bootstrap methods provide a powerful approach to statisticaldata analysis, as they have more general applications than standardparametric methods. An Introduction to Bootstrap Methods withApplications to R explores the practicality of this approach andsuccessfully utilizes R to illustrate applications for thebootstrap and other resampling methods. This book provides a modernintroduction to bootstrap methods for readers who do not have anextensive background in advanced mathematics. Emphasis throughoutis on the use of bootstrap methods as an exploratory tool,including its value in variable selection and other modelingenvironments. The authors begin with a description of bootstrap methods andits relationship to other resampling methods, along with anoverview of the wide variety of applications of the approach.Subsequent chapters offer coverage of improved confidence setestimation, estimation of error rates in discriminant analysis, andapplications to a wide variety of hypothesis testing and estimationproblems, including pharmaceutical, genomics, and economics. Toinform readers on the limitations of the method, the book alsoexhibits counterexamples to the consistency of bootstrapmethods. An introduction to R programming provides the needed preparationto work with the numerous exercises and applications presentedthroughout the book. A related website houses the book's Rsubroutines, and an extensive listing of references providesresources for further study. Discussing the topic at a remarkably practical and accessiblelevel, An Introduction to Bootstrap Methods with Applications to Ris an excellent book for introductory courses on bootstrap andresampling methods at the upper-undergraduate and graduate levels.It also serves as an insightful reference for practitioners workingwith data in engineering, medicine, and the social sciences whowould like to acquire a basic understanding of bootstrapmethods.







Bootstrap Methods

Bootstrap Methods PDF
Author: Michael R. Chernick
Publisher: Wiley-Interscience
ISBN:
Size: 20.88 MB
Format: PDF, ePub, Mobi
Category : Mathematics
Languages : en
Pages : 264
View: 3836

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Book Description:

Bootstrap Methods

by Michael R. Chernick, Bootstrap Methods Book available in PDF, EPUB, Mobi Format. Download Bootstrap Methods books, A comprehensive, practical treatment for professionals In less than two decades, the bootstrap has grown from an obscure object of theoretical study to a widely used resampling method with broad applications in numerous real-world situations. Bootstrap Methods: A Practitioner's Guide provides an introduction to the bootstrap for readers who have professional interest in these methods but do not have a background in advanced mathematics. It offers reliable, authoritative coverage of the bootstrap's considerable advantages as well as its drawbacks. This book updates classic texts in the field by presenting results on improved confidence set estimation, estimation of error rates in discriminant analysis, and applications to a wide variety of hypothesis testing and estimation problems. To alert readers to the limitations of the method, it exhibits counterexamples to the consistency of bootstrap methods. This book also makes connections between more traditional resampling methods and bootstrap. Outstanding special features of Bootstrap Methods include: * The most extensive and detailed bootstrap bibliography available, including more than 1,600 references * Discussions enlivened with stimulating topics such as data mining * Historical notes at the end of each chapter * Examples and explanations of when and why bootstrap is not effective Bootstrap Methods is a serious, useful, and unparalleled practical guide for professionals in engineering, the sciences, clinical medicine, and applied statistics.







R F R Dummies

R f  r Dummies PDF
Author: Andrie de Vries
Publisher: John Wiley & Sons
ISBN: 3527812520
Size: 26.75 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : de
Pages : 423
View: 5241

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Book Description:

R F R Dummies

by Andrie de Vries, R F R Dummies Book available in PDF, EPUB, Mobi Format. Download R F R Dummies books, Wollen Sie auch die umfangreichen Möglichkeiten von R nutzen, um Ihre Daten zu analysieren, sind sich aber nicht sicher, ob Sie mit der Programmiersprache wirklich zurechtkommen? Keine Sorge - dieses Buch zeigt Ihnen, wie es geht - selbst wenn Sie keine Vorkenntnisse in der Programmierung oder Statistik haben. Andrie de Vries und Joris Meys zeigen Ihnen Schritt für Schritt und anhand zahlreicher Beispiele, was Sie alles mit R machen können und vor allem wie Sie es machen können. Von den Grundlagen und den ersten Skripten bis hin zu komplexen statistischen Analysen und der Erstellung aussagekräftiger Grafiken. Auch fortgeschrittenere Nutzer finden in diesem Buch viele Tipps und Tricks, die Ihnen die Datenauswertung erleichtern.







Methodik Der Empirischen Forschung

Methodik der empirischen Forschung PDF
Author: Daniel Klapper
Publisher: Springer-Verlag
ISBN: 9783834904690
Size: 58.59 MB
Format: PDF, ePub, Docs
Category : Social sciences
Languages : de
Pages : 445
View: 6405

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Book Description:

Methodik Der Empirischen Forschung

by Daniel Klapper, Methodik Der Empirischen Forschung Book available in PDF, EPUB, Mobi Format. Download Methodik Der Empirischen Forschung books,







An Introduction To Statistical Learning

An Introduction to Statistical Learning PDF
Author: Gareth James
Publisher: Springer Science & Business Media
ISBN: 1461471389
Size: 39.53 MB
Format: PDF, ePub, Docs
Category : Mathematics
Languages : en
Pages : 426
View: 6162

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Book Description:

An Introduction To Statistical Learning

by Gareth James, An Introduction To Statistical Learning Book available in PDF, EPUB, Mobi Format. Download An Introduction To Statistical Learning books, An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.







Mathematical Statistics With Applications In R

Mathematical Statistics with Applications in R PDF
Author: Kandethody M. Ramachandran
Publisher: Elsevier
ISBN: 012417132X
Size: 14.21 MB
Format: PDF, Kindle
Category : Mathematics
Languages : en
Pages : 826
View: 5471

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Book Description:

Mathematical Statistics With Applications In R

by Kandethody M. Ramachandran, Mathematical Statistics With Applications In R Book available in PDF, EPUB, Mobi Format. Download Mathematical Statistics With Applications In R books, Mathematical Statistics with Applications in R, Second Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The book covers many modern statistical computational and simulation concepts that are not covered in other texts, such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. By combining the discussion on the theory of statistics with a wealth of real-world applications, the book helps students to approach statistical problem solving in a logical manner. This book provides a step-by-step procedure to solve real problems, making the topic more accessible. It includes goodness of fit methods to identify the probability distribution that characterizes the probabilistic behavior or a given set of data. Exercises as well as practical, real-world chapter projects are included, and each chapter has an optional section on using Minitab, SPSS and SAS commands. The text also boasts a wide array of coverage of ANOVA, nonparametric, MCMC, Bayesian and empirical methods; solutions to selected problems; data sets; and an image bank for students. Advanced undergraduate and graduate students taking a one or two semester mathematical statistics course will find this book extremely useful in their studies. Step-by-step procedure to solve real problems, making the topic more accessible Exercises blend theory and modern applications Practical, real-world chapter projects Provides an optional section in each chapter on using Minitab, SPSS and SAS commands Wide array of coverage of ANOVA, Nonparametric, MCMC, Bayesian and empirical methods







Neuronale Netze Selbst Programmieren

Neuronale Netze selbst programmieren PDF
Author: Tariq Rashid
Publisher: O'Reilly
ISBN: 3960101031
Size: 59.72 MB
Format: PDF, ePub
Category : Computers
Languages : de
Pages : 232
View: 2333

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Book Description:

Neuronale Netze Selbst Programmieren

by Tariq Rashid, Neuronale Netze Selbst Programmieren Book available in PDF, EPUB, Mobi Format. Download Neuronale Netze Selbst Programmieren books, Neuronale Netze sind Schlüsselelemente des Deep Learning und der Künstlichen Intelligenz, die heute zu Erstaunlichem in der Lage sind. Sie sind Grundlage vieler Anwendungen im Alltag wie beispielsweise Spracherkennung, Gesichtserkennung auf Fotos oder die Umwandlung von Sprache in Text. Dennoch verstehen nur wenige, wie neuronale Netze tatsächlich funktionieren. Dieses Buch nimmt Sie mit auf eine unterhaltsame Reise, die mit ganz einfachen Ideen beginnt und Ihnen Schritt für Schritt zeigt, wie neuronale Netze arbeiten: - Zunächst lernen Sie die mathematischen Konzepte kennen, die den neuronalen Netzen zugrunde liegen. Dafür brauchen Sie keine tieferen Mathematikkenntnisse, denn alle mathematischen Ideen werden behutsam und mit vielen Illustrationen und Beispielen erläutert. Eine Kurzeinführung in die Analysis unterstützt Sie dabei. - Dann geht es in die Praxis: Nach einer Einführung in die populäre und leicht zu lernende Programmiersprache Python bauen Sie allmählich Ihr eigenes neuronales Netz mit Python auf. Sie bringen ihm bei, handgeschriebene Zahlen zu erkennen, bis es eine Performance wie ein professionell entwickeltes Netz erreicht. - Im nächsten Schritt tunen Sie die Leistung Ihres neuronalen Netzes so weit, dass es eine Zahlenerkennung von 98 % erreicht – nur mit einfachen Ideen und simplem Code. Sie testen das Netz mit Ihrer eigenen Handschrift und werfen noch einen Blick in das mysteriöse Innere eines neuronalen Netzes. - Zum Schluss lassen Sie das neuronale Netz auf einem Raspberry Pi Zero laufen. Tariq Rashid erklärt diese schwierige Materie außergewöhnlich klar und verständlich, dadurch werden neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.







Grundlagen Der Datenanalyse Mit R

Grundlagen der Datenanalyse mit R PDF
Author: Daniel Wollschläger
Publisher: Springer-Verlag
ISBN: 3662455072
Size: 42.75 MB
Format: PDF, Docs
Category : Social Science
Languages : de
Pages : 621
View: 4984

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Book Description:

Grundlagen Der Datenanalyse Mit R

by Daniel Wollschläger, Grundlagen Der Datenanalyse Mit R Book available in PDF, EPUB, Mobi Format. Download Grundlagen Der Datenanalyse Mit R books, Dieses Buch liefert eine anwendungsorientierte Einführung in die Datenauswertung mit der freien Statistikumgebung R. Es behandelt deskriptive Auswertungen ebenso umfassend wie inferenzstatistische Analysen. Neben klassischen univariaten Verfahren berücksichtigt das Buch nonparametrische Tests, Resampling-Methoden und multivariate Statistik. Zudem deckt es die vielfältigen Möglichkeiten ab, Daten aufzubereiten und Diagramme zu erstellen. Die statistischen Verfahren werden anhand von Beispielen erläutert und an vielen Stellen mit Diagrammen illustriert. In die dritte Auflage sind sowohl die Neuerungen von R 3.1.1 als auch jüngere Entwicklungen bei den Zusatzpaketen eingeflossen. Gleichzeitig wurde der Text überarbeitet und in den Bereichen der logistischen Regression sowie der Kreuzvalidierung deutlich erweitert. Der Text behandelt nun auch die ordinale, multinomiale und Poisson-Regression ebenso wie log-lineare Modelle und die Survival-Analyse. Das Buch richtet sich an alle, die R kennenlernen und in konkreten Aufgabenstellungen einsetzen möchten, ohne bereits über Vorerfahrungen mit befehlsgesteuerten Programmen oder Programmiersprachen zu verfügen.







Resampling Methods

Resampling Methods PDF
Author: Phillip I. Good
Publisher: Springer Science & Business Media
ISBN: 9780817642433
Size: 36.73 MB
Format: PDF, ePub
Category : Mathematics
Languages : en
Pages : 238
View: 6196

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Book Description:

Resampling Methods

by Phillip I. Good, Resampling Methods Book available in PDF, EPUB, Mobi Format. Download Resampling Methods books, "Most introductory statistics books ignore or give little attention to resampling methods, and thus another generation learns the less than optimal methods of statistical analysis. Good attempts to remedy this situation by writing an introductory text that focuses on resampling methods, and he does it well."- Ron C. Fryxell, Albion College"...The wealth of the bibliography covers a wide range of disciplines."---Dr. Dimitris Karlis, Athens University of EconomicsThis thoroughly revised second edition is a practical guide to data analysis using the bootstrap, cross-validation, and permutation tests. It is an essential resource for industrial statisticians, statistical consultants, and research professionals in science, engineering, and technology.Only requiring minimal mathematics beyond algebra, it provides a table-free introduction to data analysis utilizing numerous exercises, practical data sets, and freely available statistical shareware.Topics and Features:* Offers more practical examples plus an additional chapter dedicated to regression and data mining techniques and their limitations* Uses resampling approach to introduction statistics* A practical presentation that covers all three sampling methods: bootstrap, density-estimation, and permutations* Includes systematic guide to help one select the correct procedure for a particular application* Detailed coverage of all three statistical methodologies: classification, estimation, and hypothesis testing* Suitable for classroom use and individual, self-study purposes* Numerous practical examples using popular computer programs such as SAS(r), Stata(r), and StatXact(r)* Useful appendixes with computer programs and code to develop individualized methods* Downloadable freeware from author's website: http://users.oco.net/drphilgood/resamp.htmWith its accessible style and intuitive topic development, the book is an excellent basic resource for the power, simplicity, and versatility of the bootstrap, cross-validation, and permutation tests. Students, professionals, and researchers will find it a prarticularly useful handbook for modern resampling methods and their applications.