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Author: N. Balakrishnan
Publisher:
ISBN: 9781118445112
Category : Statistics
Languages : en
Pages :
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Book Description
Wiley StatsRef: Statistics Reference Online is a comprehensive online reference resource which covers the fundamentals and applications of statistics in all fields where it is widely used. This is the most inclusive, authoritative, online reference source available in statistics. Wiley StatsRef is aimed at advanced undergraduates, postgraduates, teachers of statistics, and for experienced researchers entering a new part of the field for the first time.
Author: N. Balakrishnan
Publisher:
ISBN: 9781118445112
Category : Statistics
Languages : en
Pages :
Get Book Here
Book Description
Wiley StatsRef: Statistics Reference Online is a comprehensive online reference resource which covers the fundamentals and applications of statistics in all fields where it is widely used. This is the most inclusive, authoritative, online reference source available in statistics. Wiley StatsRef is aimed at advanced undergraduates, postgraduates, teachers of statistics, and for experienced researchers entering a new part of the field for the first time.
Author: Richard A. Levine
Publisher: John Wiley & Sons
ISBN: 1119561086
Category : Mathematics
Languages : en
Pages : 672
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Book Description
Ein unverzichtbarer Leitfaden bei der Anwendung computergestützter Statistik in der modernen Datenwissenschaft In Computational Statistics in Data Science präsentiert ein Team aus bekannten Mathematikern und Statistikern eine fundierte Zusammenstellung von Konzepten, Theorien, Techniken und Praktiken der computergestützten Statistik für ein Publikum, das auf der Suche nach einem einzigen, umfassenden Referenzwerk für Statistik in der modernen Datenwissenschaft ist. Das Buch enthält etliche Kapitel zu den wesentlichen konkreten Bereichen der computergestützten Statistik, in denen modernste Techniken zeitgemäß und verständlich dargestellt werden. Darüber hinaus bietet Computational Statistics in Data Science einen kostenlosen Zugang zu den fertigen Einträgen im Online-Nachschlagewerk Wiley StatsRef: Statistics Reference Online. Außerdem erhalten die Leserinnen und Leser: * Eine gründliche Einführung in die computergestützte Statistik mit relevanten und verständlichen Informationen für Anwender und Forscher in verschiedenen datenintensiven Bereichen * Umfassende Erläuterungen zu aktuellen Themen in der Statistik, darunter Big Data, Datenstromverarbeitung, quantitative Visualisierung und Deep Learning Das Werk eignet sich perfekt für Forscher und Wissenschaftler sämtlicher Fachbereiche, die Techniken der computergestützten Statistik auf einem gehobenen oder fortgeschrittenen Niveau anwenden müssen. Zudem gehört Computational Statistics in Data Science in das Bücherregal von Wissenschaftlern, die sich mit der Erforschung und Entwicklung von Techniken der computergestützten Statistik und statistischen Grafiken beschäftigen.
Author: Patrick Mair
Publisher: Springer
ISBN: 3319931776
Category : Social Science
Languages : en
Pages : 458
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Book Description
This textbook describes the broadening methodology spectrum of psychological measurement in order to meet the statistical needs of a modern psychologist. The way statistics is used, and maybe even perceived, in psychology has drastically changed over the last few years; computationally as well as methodologically. R has taken the field of psychology by storm, to the point that it can now safely be considered the lingua franca for statistical data analysis in psychology. The goal of this book is to give the reader a starting point when analyzing data using a particular method, including advanced versions, and to hopefully motivate him or her to delve deeper into additional literature on the method. Beginning with one of the oldest psychometric model formulations, the true score model, Mair devotes the early chapters to exploring confirmatory factor analysis, modern test theory, and a sequence of multivariate exploratory method. Subsequent chapters present special techniques useful for modern psychological applications including correlation networks, sophisticated parametric clustering techniques, longitudinal measurements on a single participant, and functional magnetic resonance imaging (fMRI) data. In addition to using real-life data sets to demonstrate each method, the book also reports each method in three parts-- first describing when and why to apply it, then how to compute the method in R, and finally how to present, visualize, and interpret the results. Requiring a basic knowledge of statistical methods and R software, but written in a casual tone, this text is ideal for graduate students in psychology. Relevant courses include methods of scaling, latent variable modeling, psychometrics for graduate students in Psychology, and multivariate methods in the social sciences.
Author: Carlos A. Coelho
Publisher: Springer Nature
ISBN: 3030287904
Category : Mathematics
Languages : en
Pages : 529
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Book Description
This book depicts a wide range of situations in which there exist finite form representations for the Meijer G and the Fox H functions. Accordingly, it will be of interest to researchers and graduate students who, when implementing likelihood ratio tests in multivariate analysis, would like to know if there exists an explicit manageable finite form for the distribution of the test statistics. In these cases, both the exact quantiles and the exact p-values of the likelihood ratio tests can be computed quickly and efficiently. The test statistics in question range from common ones, such as those used to test e.g. the equality of means or the independence of blocks of variables in real or complex normally distributed random vectors; to far more elaborate tests on the structure of covariance matrices and equality of mean vectors. The book also provides computational modules in Mathematica®, MAXIMA and R, which allow readers to easily implement, plot and compute the distributions of any of these statistics, or any other statistics that fit into the general paradigm described here.
Author: Walter W. Piegorsch
Publisher: John Wiley & Sons
ISBN: 111861965X
Category : Mathematics
Languages : en
Pages : 82
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Book Description
Statistical Data Analytics Statistical Data Analytics Foundations for Data Mining, Informatics, and Knowledge Discovery A comprehensive introduction to statistical methods for data mining and knowledge discovery Applications of data mining and ‘big data’ increasingly take center stage in our modern, knowledge-driven society, supported by advances in computing power, automated data acquisition, social media development and interactive, linkable internet software. This book presents a coherent, technical introduction to modern statistical learning and analytics, starting from the core foundations of statistics and probability. It includes an overview of probability and statistical distributions, basics of data manipulation and visualization, and the central components of standard statistical inferences. The majority of the text extends beyond these introductory topics, however, to supervised learning in linear regression, generalized linear models, and classification analytics. Finally, unsupervised learning via dimension reduction, cluster analysis, and market basket analysis are introduced. Extensive examples using actual data (with sample R programming code) are provided, illustrating diverse informatic sources in genomics, biomedicine, ecological remote sensing, astronomy, socioeconomics, marketing, advertising and finance, among many others. Statistical Data Analytics: Focuses on methods critically used in data mining and statistical informatics. Coherently describes the methods at an introductory level, with extensions to selected intermediate and advanced techniques. Provides informative, technical details for the highlighted methods. Employs the open-source R language as the computational vehicle – along with its burgeoning collection of online packages – to illustrate many of the analyses contained in the book. Concludes each chapter with a range of interesting and challenging homework exercises using actual data from a variety of informatic application areas. This book will appeal as a classroom or training text to intermediate and advanced undergraduates, and to beginning graduate students, with sufficient background in calculus and matrix algebra. It will also serve as a source-book on the foundations of statistical informatics and data analytics to practitioners who regularly apply statistical learning to their modern data.
Author: Tadashi Imaizumi
Publisher: Springer Nature
ISBN: 9811533113
Category : Mathematics
Languages : en
Pages : 506
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Book Description
This edited volume focuses on the latest developments in classification and data science and covers a wide range of topics in the context of data analysis and related areas, e.g. the analysis of complex data, analysis of qualitative data, methods for high-dimensional data, dimensionality reduction, data visualization, multivariate statistical methods, and various applications to real data in the social sciences, medical sciences, and other disciplines. In addition to sharing theoretical and methodological findings, the book shows how to apply the proposed methods to a variety of problems — e.g. in consumer behavior, decision-making, marketing data and social network structures. Both methodological aspects and applications to a wide range of areas such as economics, behavioral science, marketing science, management science and the social sciences are covered. The book is chiefly intended for researchers and practitioners who are interested in the latest developments and practical applications in these fields, as well as applied statisticians and data analysts. Its combination of methodological advances with a wide range of real-world applications gathered from several fields makes it of unique value in helping readers solve their research problems.
Author: Alessandra Petrucci
Publisher: Springer Nature
ISBN: 3030211584
Category : Computers
Languages : en
Pages : 479
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Book Description
This volume collects the extended versions of papers presented at the SIS Conference “Statistics and Data Science: new challenges, new generations”, held in Florence, Italy on June 28-30, 2017. Highlighting the central role of statistics and data analysis methods in the era of Data Science, the contributions offer an essential overview of the latest developments in various areas of statistics research. The 35 contributions have been divided into six parts, each of which focuses on a core area contributing to “Data Science”. The book covers topics including strong statistical methodologies, Bayesian approaches, applications in population and social studies, studies in economics and finance, techniques of sample design and mathematical statistics. Though the book is mainly intended for researchers interested in the latest frontiers of Statistics and Data Analysis, it also offers valuable supplementary material for students of the disciplines dealt with here. Lastly, it will help Statisticians and Data Scientists recognize their counterparts’ fundamental role.
Author: Kevin Daimi
Publisher: Springer Nature
ISBN: 3031655222
Category :
Languages : en
Pages : 794
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Book Description
Author: Aicke Hinrichs
Publisher: Springer Nature
ISBN: 3031597621
Category :
Languages : en
Pages : 657
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Book Description
Author: Ritu Arora
Publisher: Springer
ISBN: 3319337424
Category : Computers
Languages : en
Pages : 328
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Book Description
This book provides an overview of the resources and research projects that are bringing Big Data and High Performance Computing (HPC) on converging tracks. It demystifies Big Data and HPC for the reader by covering the primary resources, middleware, applications, and tools that enable the usage of HPC platforms for Big Data management and processing.Through interesting use-cases from traditional and non-traditional HPC domains, the book highlights the most critical challenges related to Big Data processing and management, and shows ways to mitigate them using HPC resources. Unlike most books on Big Data, it covers a variety of alternatives to Hadoop, and explains the differences between HPC platforms and Hadoop.Written by professionals and researchers in a range of departments and fields, this book is designed for anyone studying Big Data and its future directions. Those studying HPC will also find the content valuable.
