Model Selection and Multimodel Inference

A Practical Information-Theoretic Approach

Author: Kenneth P. Burnham,David R. Anderson

Publisher: Springer Science & Business Media

ISBN: 0387224564

Category: Mathematics

Page: 488

View: 9479

A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.
Posted in Mathematics

Wissenschaftliche Abhandlungen

Author: Ludwig Boltzmann

Publisher: American Mathematical Soc.

ISBN: 9780821829264

Category:

Page: 1953

View: 7814

Posted in

Model Selection and Inference

A Practical Information-theoretic Approach

Author: Kenneth P. Burnham,David Raymond Anderson

Publisher: Springer Science & Business Media

ISBN: 9780387985046

Category: Biologie - Modèles mathématiques

Page: 353

View: 5608

This book is unique in that it covers the philosophy of model-based data analysis and a strategy for the analysis of empirical data. The book introduces information-theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. The book presents several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. This is an applied book written primarily for biologists and statisticians using models for making inferences from empirical data. People interested in the empirical sciences will find this material useful as it offers an alternative to hypothesis testing and Bayesian approaches.
Posted in Biologie - Modèles mathématiques

Grundkurs Mathematik in den Biowissenschaften

Author: Hans-Andreas Braunß,Heinz Junek,Thomas Krainer

Publisher: Springer-Verlag

ISBN: 3764377100

Category: Mathematics

Page: 208

View: 2416

Geeignet als Referenz wie auch als Begleitbuch für einen 1-2 semestrigen Kurs. Im Zentrum stehen Funktionen in einer reellen Variablen und ihre Anwendungen in den Biowissenschaften, insbesondere Fourieranalyse und Differentialgleichungsmodelle. Eine Vielzahl von Aufgaben dient der Vertiefung und aktiven Aneignung des Stoffes.
Posted in Mathematics

Medizinische Statistik

Author: Hans J. Trampisch,Jürgen Windeler

Publisher: Springer-Verlag

ISBN: 364256996X

Category: Mathematics

Page: 376

View: 5839

"Statistiken sind merkwürdige Dinge ...", dies wird so mancher Mediziner denken, wenn er sich mit der Biometrie befaßt. Sei es im Rahmen seiner Ausbildung oder im Zuge wissenschaftlicher oder klinischer Studien, Kenntnisse der Statistik und Mathematik sind unentbehrlich für die tägliche Arbeit des Mediziners. Ziel dieses Lehrbuches ist es, den Mediziner systematisch an biometrische Terminologie und Arbeitsmethoden heranzuführen, um ihn schließlich mit den Grundlagen der Wahrscheinlichkeitsrechung vertraut zu machen. Nach der Lektüre dieses Buches hält der Leser ein Werkzeug in den Händen, das ihm bei der Lösung medizinscher Fragestellungen hilft ebenso wie bei der Beschreibung von Ergebnissen wissenschaftlicher Studien und natürlich bei der Doktorarbeit!
Posted in Mathematics

Nichtlineare Systeme und Regelungen

Author: Jürgen Adamy

Publisher: Springer-Verlag

ISBN: 364245013X

Category: Technology & Engineering

Page: 614

View: 5429

Dieses Lehrbuch gibt eine anschauliche Einführung in die Theorie und Anwendung nichtlinearer Regelungen. Der Autor stellt die in Forschung und industrieller Anwendung immer wichtiger werdenden Verfahren der nichtlinearen Regelungen vor und erläutert sie. Wesentliche Merkmale sind die gute Verständlichkeit der Darstellung sowie die hervorragenden Abbildungen. Die praktische Bedeutung der beschriebenen Regelungen wird anhand zahlreicher Beispiele illustriert. Gegenüber der ersten Auflage ist die zweite überarbeitet und wesentlich erweitert. Systemeigenschaften wie die Steuerbarkeit, die Flachheit und die Passivität wurden hinzugenommen. Neben der flachheitsbasierten und der passivitätsbasierten Regelung sind nun auch die exakte Linearisierung im MIMO-Fall und die exakte Zustandslinearisierung Teil des Buches. Entsprechend den hinzugekommenen Systemeigenschaften wurde der Titel von "Nichtlineare Regelungen" in "Nichtlineare Systeme und Regelungen" geändert. Das Buch richtet sich sowohl an Studierende der Elektrotechnik, Informationstechnik und des Maschinenbaus als auch an Ingenieure in der Industrie.
Posted in Technology & Engineering

Stichprobenverfahren

Author: William G. Cochran

Publisher: Walter de Gruyter

ISBN: 3110823004

Category: Reference

Page: 474

View: 1596

Posted in Reference

Modeling Demographic Processes in Marked Populations

Author: David L. Thomson,Evan G. Cooch,Michael J. Conroy

Publisher: Springer Science & Business Media

ISBN: 9780387781518

Category: Medical

Page: 1132

View: 544

Here, biologists and statisticians come together in an interdisciplinary synthesis with the aim of developing new methods to overcome the most significant challenges and constraints faced by quantitative biologists seeking to model demographic rates.
Posted in Medical

Statistik im Forschungsprozess

Eine Philosophie der Statistik als Baustein einer integrativen Wissenschaftstheorie

Author: Uwe Saint-Mont

Publisher: Springer-Verlag

ISBN: 3790827231

Category: Mathematics

Page: 675

View: 2238

Statistik ist die Wissenschaft, Philosophie und Kunst der Datenanalyse. Wie die Physik besitzt sie sowohl einen mathematischen Kern als auch eine empirische Fundierung und ihre zumeist quantitativen Argumente verknüpfen spezifische Daten mit allgemeinen Theorien. Mit Statistik gekonnt umzugehen bedeutet nicht nur, Daten effizient zu erheben oder problemadäquat zu modellieren. Die Organisation des gesamten Informationsflusses – von der substanziellen Fragestellung zur empirischen Untersuchung und wieder zurück – ist entscheidend. Das vorliegende Buch stellt die Statistik im Spannungsfeld von empirischen Wissenschaften, Mathematik, Informatik und Wissenschaftstheorie dar. Geschrieben für Akademiker aller genannten Gebiete zeigt es Parallelen zwischen vermeintlich isolierten Feldern auf und verdichtet diese zu generellen methodischen Prinzipien. So wird Statistik zur facettenreichen Wissenschaft - omnipräsentes Werkzeug im Forschungsprozess, angewandte Mathematik und Philosophie in einem.
Posted in Mathematics

Stable Solution of Inverse Problems

Author: Johann Baumeister

Publisher: Vieweg + Teubner Verlag

ISBN: N.A

Category: Language Arts & Disciplines

Page: 254

View: 7923

These notes are intended to describe the basic concepts of solving inverse problems in a stable way. Since almost all in­ verse problems are ill-posed in its original formulation the discussion of methods to overcome difficulties which result from this fact is the main subject of this book. Over the past fifteen years, the number of publications on inverse problems has grown rapidly. Therefore, these notes can be neither a comprehensive introduction nor a complete mono­ graph on the topics considered; it is designed to provide the main ideas and methods. Throughout, we have not striven for the most general statement, but the clearest one which would cover the most situations. The presentation is intended to be accessible to students whose mathematical background includes basic courses in ad­ vanced calculus, linear algebra and functional analysis. Each chapter contains bibliographical comments. At the end of Chap­ ter 1 references are given which refer to topics which are not studied in this book. I am very grateful to Mrs. B. Brodt for typing and to W. Scondo and u. Schuch for inspecting the manuscript.
Posted in Language Arts & Disciplines

Data Mining Algorithms

Explained Using R

Author: Pawel Cichosz

Publisher: John Wiley & Sons

ISBN: 1118950801

Category: Mathematics

Page: 720

View: 717

Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.
Posted in Mathematics

Ecological Statistics

Contemporary theory and application

Author: Gordon A. Fox,Simoneta Negrete-Yankelevich,Vinicio J. Sosa

Publisher: OUP Oxford

ISBN: 0191652881

Category: Science

Page: 400

View: 5372

The application and interpretation of statistics are central to ecological study and practice. Ecologists are now asking more sophisticated questions than in the past. These new questions, together with the continued growth of computing power and the availability of new software, have created a new generation of statistical techniques. These have resulted in major recent developments in both our understanding and practice of ecological statistics. This novel book synthesizes a number of these changes, addressing key approaches and issues that tend to be overlooked in other books such as missing/censored data, correlation structure of data, heterogeneous data, and complex causal relationships. These issues characterize a large proportion of ecological data, but most ecologists' training in traditional statistics simply does not provide them with adequate preparation to handle the associated challenges. Uniquely, Ecological Statistics highlights the underlying links among many statistical approaches that attempt to tackle these issues. In particular, it gives readers an introduction to approaches to inference, likelihoods, generalized linear (mixed) models, spatially or phylogenetically-structured data, and data synthesis, with a strong emphasis on conceptual understanding and subsequent application to data analysis. Written by a team of practicing ecologists, mathematical explanations have been kept to the minimum necessary. This user-friendly textbook will be suitable for graduate students, researchers, and practitioners in the fields of ecology, evolution, environmental studies, and computational biology who are interested in updating their statistical tool kits. A companion web site provides example data sets and commented code in the R language.
Posted in Science

Modeling Techniques in Predictive Analytics with Python and R

A Guide to Data Science

Author: Thomas W. Miller

Publisher: FT Press

ISBN: 013389214X

Category: Computers

Page: 448

View: 5437

Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more
Posted in Computers

Modeling Techniques in Predictive Analytics

Business Problems and Solutions with R, Revised and Expanded Edition

Author: Thomas W. Miller

Publisher: FT Press

ISBN: 0133886190

Category: Computers

Page: 384

View: 5231

To succeed with predictive analytics, you must understand it on three levels: Strategy and management Methods and models Technology and code This up-to-the-minute reference thoroughly covers all three categories. Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer, or manager, it will teach you crucial skills you don’t yet have. Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations–not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work–and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively. All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller If you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business cases and challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods. Gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more
Posted in Computers

Metabolic Ecology

A Scaling Approach

Author: Richard M. Sibly,James H. Brown,Astrid Kodric-Brown

Publisher: John Wiley & Sons

ISBN: 1119968518

Category: Science

Page: 256

View: 4244

One of the first textbooks in this emerging important field of ecology. Most of ecology is about metabolism: the ways that organisms use energy and materials. The energy requirements of individuals – their metabolic rates – vary predictably with their body size and temperature. Ecological interactions are exchanges of energy and materials between organisms and their environments. So metabolic rate affects ecological processes at all levels: individuals, populations, communities and ecosystems. Each chapter focuses on a different process, level of organization, or kind of organism. It lays a conceptual foundation and presents empirical examples. Together, the chapters provide an integrated framework that holds the promise for a unified theory of ecology. The book is intended to be accessible to upper-level undergraduate, and graduate students, but also of interest to senior scientists. Its easy-to-read chapters and clear illustrations can be used in lecture and seminar courses. Together they make for an authoritative treatment that will inspire future generations to study metabolic ecology.
Posted in Science