-PDF Download- Statistical Paradigms EBOOK

Statistical Paradigms: Recent Advances And Reconciliations

Author: Ashis Sengupta

Publisher: World Scientific

Published: 2014-10-03

Total Pages: 308

ISBN-13: 9814644110

This volume consists of a collection of research articles on classical and emerging Statistical Paradigms — parametric, non-parametric and semi-parametric, frequentist and Bayesian — encompassing both theoretical advances and emerging applications in a variety of scientific disciplines. For advances in theory, the topics include: Bayesian Inference, Directional Data Analysis, Distribution Theory, Econometrics and Multiple Testing Procedures. The areas in emerging applications include: Bioinformatics, Factorial Experiments and Linear Models, Hotspot Geoinformatics and Reliability.

Statistical Evidence

Author: Richard Royall

Publisher: Routledge

Published: 2017-11-22

Total Pages: 258

ISBN-13: 1351414550

DOWNLOAD EBOOK →

Interpreting statistical data as evidence, Statistical Evidence: A Likelihood Paradigm focuses on the law of likelihood, fundamental to solving many of the problems associated with interpreting data in this way. Statistics has long neglected this principle, resulting in a seriously defective methodology. This book redresses the balance, explaining why science has clung to a defective methodology despite its well-known defects. After examining the strengths and weaknesses of the work of Neyman and Pearson and the Fisher paradigm, the author proposes an alternative paradigm which provides, in the law of likelihood, the explicit concept of evidence missing from the other paradigms. At the same time, this new paradigm retains the elements of objective measurement and control of the frequency of misleading results, features which made the old paradigms so important to science. The likelihood paradigm leads to statistical methods that have a compelling rationale and an elegant simplicity, no longer forcing the reader to choose between frequentist and Bayesian statistics.

Utilizing Big Data Paradigms for Business Intelligence

Author: Darmont, Jérôme

Publisher: IGI Global

Published: 2018-08-10

Total Pages: 313

ISBN-13: 1522549641

DOWNLOAD EBOOK →

Because efficient compilation of information allows managers and business leaders to make the best decisions for the financial solvency of their organizations, data analysis is an important part of modern business administration. Understanding the use of analytics, reporting, and data mining in everyday business environments is imperative to the success of modern businesses. Utilizing Big Data Paradigms for Business Intelligence is a pivotal reference source that provides vital research on how to address the challenges of data extraction in business intelligence using the five “Vs” of big data: velocity, volume, value, variety, and veracity. This book is ideally designed for business analysts, investors, corporate managers, entrepreneurs, and researchers in the fields of computer science, data science, and business intelligence.

Data Mining: Foundations and Intelligent Paradigms

Author: Dawn E. Holmes

Publisher: Springer Science & Business Media

Published: 2011-11-09

Total Pages: 257

ISBN-13: 3642232418

DOWNLOAD EBOOK →

There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled “DATA MINING: Foundations and Intelligent Paradigms: Volume 2: Core Topics including Statistical, Time-Series and Bayesian Analysis” we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.

Christian and Humanist Foundations for Statistical Inference

Author: Andrew M. Hartley

Publisher: Wipf and Stock Publishers

Published: 2007-12-01

Total Pages: 138

ISBN-13: 1556355491

DOWNLOAD EBOOK →

The Philosophy of the Law Idea (PLI) analyzes the manner in which religious beliefs control scientific theorizing. Religious beliefs control philosophical overviews of reality. Overviews of reality, also called ontologies, try to discover and disclose the essential nature of reality. They are concerned with what kinds of things exist and with the connections between the various types of properties and laws in human experience. Among such overviews are the biblically consistent overview provided by the PLI and certain humanist mathematicist and subjectivist overviews. The science of statistical inference seeks to evaluate the credibility of scientific hypotheses given empirical data. This essay reviews various popular paradigms, or systems of theories, concerning the ways that credibility may be evaluated, and identifies some ways that these religiously controlled overviews of reality have, in turn, controlled statistical paradigms. In particular, one paradigm harmonizes with the PLI's overview; another, with the subjectivist overview; and two others, with the mathematicist overview.

Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges

Author: Aboul Ella Hassanien

Publisher: Springer Nature

Published: 2020-12-14

Total Pages: 648

ISBN-13: 303059338X

DOWNLOAD EBOOK →

This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications.

Research Paradigms and Their Methodological Alignment in Social Sciences

Author: Bunmi Isaiah Omodan

Publisher: Taylor & Francis

Published: 2024-08-01

Total Pages: 233

ISBN-13: 1040093043

DOWNLOAD EBOOK →

Research Paradigms and Their Methodological Alignment in Social Sciences is a comprehensive guide addressing the common conceptions surrounding research paradigms. This practical book demystifies complex concepts, giving researchers a nuanced understanding of the significance of research paradigms. It offers detailed insights, examples, and strategies for selecting and applying appropriate research methods, aiming to enhance the rigour and impact of scholarly work. This insightful guide meticulously explores the intricacies of research paradigms in the social sciences. It begins by unravelling the concept and historical development of research paradigm, emphasising its pivotal role in shaping the research process. The book elucidates major research paradigms, including positivism, interpretivism, transformative paradigm, postcolonial indigenous paradigm, and pragmatism. Each paradigm is dissected, unveiling philosophical underpinnings, methodological designs, and critical considerations. The chapters carefully align research questions with specific paradigms through illustrative case studies, offering practical guidance for researchers at all levels. Notably, the transformative paradigm and postcolonial indigenous perspective receive dedicated attention, addressing their unique methodological nuances and ethical dimensions. The exploration extends to pragmatism, seamlessly integrating theoretical foundations with real-world applications. The book strives to bridge the awareness gap in academic settings, fostering a profound appreciation for research paradigms and promoting a thoughtful, rigorous approach to scholarly inquiry. This book caters to students, novice and experienced researchers, offering a comprehensive understanding of research paradigms. It's valuable for academia, aiding undergraduate and postgraduate students, educators, and researchers in various disciplines. Research organisations, academic institutions, and professionals in diverse fields engaged in research and development will also find it a valuable resource.

Machine Learning Paradigms

Author: Aristomenis S. Lampropoulos

Publisher: Springer

Published: 2015-06-13

Total Pages: 135

ISBN-13: 3319191357

DOWNLOAD EBOOK →

This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It is exactly this problem that the authors address in the monograph at hand. Specifically, the books approach is based on one-class classification methodologies that have been appearing in recent machine learning research. The blending of recommender systems and one-class classification provides a new very fertile field for research, innovation and development with potential applications in “big data” as well as “sparse data” problems. The book will be useful to researchers, practitioners and graduate students dealing with problems of extensive and complex data. It is intended for both the expert/researcher in the fields of Pattern Recognition, Machine Learning and Recommender Systems, as well as for the general reader in the fields of Applied and Computer Science who wishes to learn more about the emerging discipline of Recommender Systems and their applications. Finally, the book provides an extended list of bibliographic references which covers the relevant literature completely.

Statistical Evidence

Author: Richard M. Royall

Publisher:

Published: 1997

Total Pages: 191

ISBN-13:

DOWNLOAD EBOOK →

Philosophy of Statistics

Author:

Publisher: Elsevier

Published: 2011-05-31

Total Pages: 1260

ISBN-13: 9780080930961

DOWNLOAD EBOOK →

Statisticians and philosophers of science have many common interests but restricted communication with each other. This volume aims to remedy these shortcomings. It provides state-of-the-art research in the area of philosophy of statistics by encouraging numerous experts to communicate with one another without feeling “restricted by their disciplines or thinking “piecemeal in their treatment of issues. A second goal of this book is to present work in the field without bias toward any particular statistical paradigm. Broadly speaking, the essays in this Handbook are concerned with problems of induction, statistics and probability. For centuries, foundational problems like induction have been among philosophers’ favorite topics; recently, however, non-philosophers have increasingly taken a keen interest in these issues. This volume accordingly contains papers by both philosophers and non-philosophers, including scholars from nine academic disciplines. Provides a bridge between philosophy and current scientific findings Covers theory and applications Encourages multi-disciplinary dialogue