Innovations in Multivariate Statistical Modeling
Innovations in Multivariate Statistical Modeling

Author: Andriëtte Bekker

Publisher: Springer Nature

Published: 2022-12-15

Total Pages: 434

ISBN-13: 3031139712

DOWNLOAD EBOOK

Multivariate statistical analysis has undergone a rich and varied evolution during the latter half of the 20th century. Academics and practitioners have produced much literature with diverse interests and with varying multidisciplinary knowledge on different topics within the multivariate domain. Due to multivariate algebra being of sustained interest and being a continuously developing field, its appeal breaches laterally across multiple disciplines to act as a catalyst for contemporary advances, with its core inferential genesis remaining in that of statistics. It is exactly this varied evolution caused by an influx in data production, diffusion, and understanding in scientific fields that has blurred many lines between disciplines. The cross-pollination between statistics and biology, engineering, medical science, computer science, and even art, has accelerated the vast amount of questions that statistical methodology has to answer and report on. These questions are often multivariate in nature, hoping to elucidate uncertainty on more than one aspect at the same time, and it is here where statistical thinking merges mathematical design with real life interpretation for understanding this uncertainty. Statistical advances benefit from these algebraic inventions and expansions in the multivariate paradigm. This contributed volume aims to usher novel research emanating from a multivariate statistical foundation into the spotlight, with particular significance in multidisciplinary settings. The overarching spirit of this volume is to highlight current trends, stimulate a focus on, and connect multidisciplinary dots from and within multivariate statistical analysis. Guided by these thoughts, a collection of research at the forefront of multivariate statistical thinking is presented here which has been authored by globally recognized subject matter experts.

Modern Multivariate Statistical Techniques
Modern Multivariate Statistical Techniques

Author: Alan J. Izenman

Publisher: Springer Science & Business Media

Published: 2009-03-02

Total Pages: 757

ISBN-13: 0387781897

DOWNLOAD EBOOK

This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before.

Methods for Statistical Data Analysis of Multivariate Observations
Methods for Statistical Data Analysis of Multivariate Observations

Author: R. Gnanadesikan

Publisher: John Wiley & Sons

Published: 2011-01-25

Total Pages: 386

ISBN-13: 1118030923

DOWNLOAD EBOOK

A practical guide for multivariate statistical techniques-- nowupdated and revised In recent years, innovations in computer technology and statisticalmethodologies have dramatically altered the landscape ofmultivariate data analysis. This new edition of Methods forStatistical Data Analysis of Multivariate Observations explorescurrent multivariate concepts and techniques while retaining thesame practical focus of its predecessor. It integrates methods anddata-based interpretations relevant to multivariate analysis in away that addresses real-world problems arising in many areas ofinterest. Greatly revised and updated, this Second Edition provides helpfulexamples, graphical orientation, numerous illustrations, and anappendix detailing statistical software, including the S (or Splus)and SAS systems. It also offers * An expanded chapter on cluster analysis that covers advances inpattern recognition * New sections on inputs to clustering algorithms and aids forinterpreting the results of cluster analysis * An exploration of some new techniques of summarization andexposure * New graphical methods for assessing the separations among theeigenvalues of a correlation matrix and for comparing sets ofeigenvectors * Knowledge gained from advances in robust estimation anddistributional models that are slightly broader than themultivariate normal This Second Edition is invaluable for graduate students, appliedstatisticians, engineers, and scientists wishing to usemultivariate techniques in a variety of disciplines.

Applied Statistics and Multivariate Data Analysis for Business and Economics
Applied Statistics and Multivariate Data Analysis for Business and Economics

Author: Thomas Cleff

Publisher: Springer

Published: 2019-07-10

Total Pages: 488

ISBN-13: 303017767X

DOWNLOAD EBOOK

This textbook will familiarize students in economics and business, as well as practitioners, with the basic principles, techniques, and applications of applied statistics, statistical testing, and multivariate data analysis. Drawing on practical examples from the business world, it demonstrates the methods of univariate, bivariate, and multivariate statistical analysis. The textbook covers a range of topics, from data collection and scaling to the presentation and simple univariate analysis of quantitative data, while also providing advanced analytical procedures for assessing multivariate relationships. Accordingly, it addresses all topics typically covered in university courses on statistics and advanced applied data analysis. In addition, it does not limit itself to presenting applied methods, but also discusses the related use of Excel, SPSS, and Stata.

Multivariate Statistical Machine Learning Methods for Genomic Prediction
Multivariate Statistical Machine Learning Methods for Genomic Prediction

Author: Osval Antonio Montesinos López

Publisher: Springer Nature

Published: 2022-02-14

Total Pages: 707

ISBN-13: 3030890104

DOWNLOAD EBOOK

This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.

Statistical Modeling for Management
Statistical Modeling for Management

Author: Graeme D Hutcheson

Publisher: SAGE

Published: 2008-02-12

Total Pages: 255

ISBN-13: 1849202486

DOWNLOAD EBOOK

Bringing to life the most widely used quantitative measurements and statistical techniques in marketing, this book is packed with user-friendly descriptions, examples and study applications. The process of making marketing decisions is frequently dependent on quantitative analysis and the use of specific statistical tools and techniques which can be tailored and adapted to solve particular marketing problems. Any student hoping to enter the world of marketing will need to show that they understand and have mastered these techniques. A bank of downloadable data sets to compliment the tables provided in the textbook are provided free for you.

Advances in Statistics - Theory and Applications
Advances in Statistics - Theory and Applications

Author: Indranil Ghosh

Publisher: Springer Nature

Published: 2021-04-01

Total Pages: 443

ISBN-13: 3030629007

DOWNLOAD EBOOK

This edited collection brings together internationally recognized experts in a range of areas of statistical science to honor the contributions of the distinguished statistician, Barry C. Arnold. A pioneering scholar and professor of statistics at the University of California, Riverside, Dr. Arnold has made exceptional advancements in different areas of probability, statistics, and biostatistics, especially in the areas of distribution theory, order statistics, and statistical inference. As a tribute to his work, this book presents novel developments in the field, as well as practical applications and potential future directions in research and industry. It will be of interest to graduate students and researchers in probability, statistics, and biostatistics, as well as practitioners and technicians in the social sciences, economics, engineering, and medical sciences.

Technology Innovation in Manufacturing
Technology Innovation in Manufacturing

Author: Davinder Singh

Publisher: CRC Press

Published: 2022-12-14

Total Pages: 160

ISBN-13: 1000809714

DOWNLOAD EBOOK

This text identifies and discusses different technology innovation initiatives (TIIs) such as entrepreneurial capability, technology infrastructure capability, organizational culture and climate, and government initiatives. It further evaluates the relationship between various technology innovation initiatives and manufacturing performances using multi-criteria decision-making techniques such as fuzzy set theory (FST), structural equation modeling (SEM), and analytic hierarchy process (AHP). It will serve as an ideal reference text for graduate students and academic researchers in the field of industrial engineering, manufacturing engineering, mechanical engineering, automotive engineering. This book: • Discusses technology innovation initiatives such as entrepreneurial capability, technology infrastructure capability, and organizational culture. • Highlights technology innovation-strategy model in assisting manufacturing industries for enhancing their performance in today’s competitive environment. • Examines the effect of technology innovation initiatives on the performance of manufacturing industries. • Covers multi-criteria decision-making techniques such as fuzzy set theory, structural equation modeling, and analytic hierarchy process. • Explores the validation of fuzzy-based technology innovation model through structural equation modeling.