Applications In Statistical Computing
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Applications In Statistical Computing
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Author : Nadja Bauer
language : en
Publisher: Springer Nature
Release Date : 2019-10-12
Applications In Statistical Computing written by Nadja Bauer and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-12 with Computers categories.
This volume presents a selection of research papers on various topics at the interface of statistics and computer science. Emphasis is put on the practical applications of statistical methods in various disciplines, using machine learning and other computational methods. The book covers fields of research including the design of experiments, computational statistics, music data analysis, statistical process control, biometrics, industrial engineering, and econometrics. Gathering innovative, high-quality and scientifically relevant contributions, the volume was published in honor of Claus Weihs, Professor of Computational Statistics at TU Dortmund University, on the occasion of his 66th birthday.
Reliability And Statistical Computing
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Author : Hoang Pham
language : en
Publisher: Springer Nature
Release Date : 2020-03-28
Reliability And Statistical Computing written by Hoang Pham and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-28 with Technology & Engineering categories.
This book presents the latest developments in both qualitative and quantitative computational methods for reliability and statistics, as well as their applications. Consisting of contributions from active researchers and experienced practitioners in the field, it fills the gap between theory and practice and explores new research challenges in reliability and statistical computing. The book consists of 18 chapters. It covers (1) modeling in and methods for reliability computing, with chapters dedicated to predicted reliability modeling, optimal maintenance models, and mechanical reliability and safety analysis; (2) statistical computing methods, including machine learning techniques and deep learning approaches for sentiment analysis and recommendation systems; and (3) applications and case studies, such as modeling innovation paths of European firms, aircraft components, bus safety analysis, performance prediction in textile finishing processes, and movie recommendation systems. Given its scope, the book will appeal to postgraduates, researchers, professors, scientists, and practitioners in a range of fields, including reliability engineering and management, maintenance engineering, quality management, statistics, computer science and engineering, mechanical engineering, business analytics, and data science.
Computational And Statistical Methods For Analysing Big Data With Applications
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Author : Shen Liu
language : en
Publisher: Academic Press
Release Date : 2015-11-20
Computational And Statistical Methods For Analysing Big Data With Applications written by Shen Liu and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-20 with Mathematics categories.
Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration. Computational and Statistical Methods for Analysing Big Data with Applications starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data. - Advanced computational and statistical methodologies for analysing big data are developed - Experimental design methodologies are described and implemented to make the analysis of big data more computationally tractable - Case studies are discussed to demonstrate the implementation of the developed methods - Five high-impact areas of application are studied: computer vision, geosciences, commerce, healthcare and transportation - Computing code/programs are provided where appropriate
Computational Statistics In Data Science
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Author : Walter W. Piegorsch
language : de
Publisher: John Wiley & Sons
Release Date : 2022-03-23
Computational Statistics In Data Science written by Walter W. Piegorsch and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-23 with Mathematics categories.
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.
Methodologies And Applications Of Computational Statistics For Machine Intelligence
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Author : Debabrata Samanta
language : en
Publisher: Engineering Science Reference
Release Date : 2021
Methodologies And Applications Of Computational Statistics For Machine Intelligence written by Debabrata Samanta and has been published by Engineering Science Reference this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Machine learning categories.
"This book delves into computational statistics that focus on devising an efficient methodology to obtain quantitative solutions for problems that are devised quantitatively and brings together computational capability and statistical advanced thought processes to solve some of the problems encountered in the field"--
Statistical Computing With R
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Author : Maria L. Rizzo
language : en
Publisher: CRC Press
Release Date : 2007-11-15
Statistical Computing With R written by Maria L. Rizzo and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-11-15 with Reference categories.
Computational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems, making the versatile R language an ideal computing environment for these fields. One of the first books on these topics to feature R, Statistical Computing with R covers the traditiona
Statistical Computing Environments For Social Research
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Author : Robert Stine
language : en
Publisher: SAGE Publications, Incorporated
Release Date : 1997
Statistical Computing Environments For Social Research written by Robert Stine and has been published by SAGE Publications, Incorporated this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Reference categories.
The nature of statistics has changed from classical notions of hypothesis testing toward graphical and exploratory data analysis that exploits the flexibility of interactive computing and graphical displays. With contributions from some of the leading researchers in the social sciences and statistics, Statistical Computing Environments for Social Research describes seven statistical computing environments--APL2STAT, GAUSS, Lisp-Stat, Mathematica, S, SAS/IML, and Stata--that can be used effectively in graphical and exploratory modeling. These statistical computing environments, in contrast to a standard statistical package, provide programming tools for building other statistical applications. Programmability, flexible data structures, and--in the case of some of the computing environments--graphical interfaces and object-oriented programming permit researchers to take advantage of emerging statistical methodologies. Three additional chapters, describing the Axis, R-code, and ViSta statistical packages, demonstrate how researchers have extended one of the computing environments--Lisp-Stat--to produce significant statistical applications employing graphical interfaces to statistical software. To illustrate the capabilities of the seven statistical computing environments, each contributor uses the same data set to perform three computing tasks: robust regression, bootstrap resampling, and kernel-density estimation. The same data are analyzed in the chapters on Axis, R-code, and ViSta packages. The chapters in Statistical Computing Environments for Social Research illustrate important ideas and techniques in modern data analysis and statistical computing, ideas and techniques that readers will be able to apply in the more effective analysis of their own data.
Handbook Of Parallel Computing And Statistics
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Author : Erricos John Kontoghiorghes
language : en
Publisher: CRC Press
Release Date : 2005-12-21
Handbook Of Parallel Computing And Statistics written by Erricos John Kontoghiorghes and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-12-21 with Computers categories.
Technological improvements continue to push back the frontier of processor speed in modern computers. Unfortunately, the computational intensity demanded by modern research problems grows even faster. Parallel computing has emerged as the most successful bridge to this computational gap, and many popular solutions have emerged based on its concepts
Probabilistic And Statistical Methods In Computer Science
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Author : Jean-François Mari
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-04-17
Probabilistic And Statistical Methods In Computer Science written by Jean-François Mari and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-17 with Mathematics categories.
Probabilistic and Statistical Methods in Computer Science presents a large variety of applications of probability theory and statistics in computer science and more precisely in algorithm analysis, speech recognition and robotics. It is written on a self-contained basis: all probabilistic and statistical tools needed are introduced on a comprehensible level. In addition all examples are worked out completely. Most of the material is scattered throughout available literature. However, this is the first volume that brings together all of this material in such an accessible format. Probabilistic and Statistical Methods in Computer Science is intended for students in computer science and applied mathematics, for engineers and for all researchers interested in applications of probability theory and statistics. It is suitable for self study as well as being appropriate for a course or seminar.