New Advances In Statistics And Data Science

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New Advances in Statistics and Data Science

Author : Ding-Geng Chen
Publisher : Springer
ISBN 13 : 3319694162
Total Pages : 348 pages
Book Rating : 4.3/5 (196 download)

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Book Synopsis New Advances in Statistics and Data Science by : Ding-Geng Chen

Download or read book New Advances in Statistics and Data Science written by Ding-Geng Chen and published by Springer. This book was released on 2018-01-17 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is comprised of the presentations delivered at the 25th ICSA Applied Statistics Symposium held at the Hyatt Regency Atlanta, on June 12-15, 2016. This symposium attracted more than 700 statisticians and data scientists working in academia, government, and industry from all over the world. The theme of this conference was the “Challenge of Big Data and Applications of Statistics,” in recognition of the advent of big data era, and the symposium offered opportunities for learning, receiving inspirations from old research ideas and for developing new ones, and for promoting further research collaborations in the data sciences. The invited contributions addressed rich topics closely related to big data analysis in the data sciences, reflecting recent advances and major challenges in statistics, business statistics, and biostatistics. Subsequently, the six editors selected 19 high-quality presentations and invited the speakers to prepare full chapters for this book, which showcases new methods in statistics and data sciences, emerging theories, and case applications from statistics, data science and interdisciplinary fields. The topics covered in the book are timely and have great impact on data sciences, identifying important directions for future research, promoting advanced statistical methods in big data science, and facilitating future collaborations across disciplines and between theory and practice.

Advanced Statistical Methods in Data Science

Author : Ding-Geng Chen
Publisher : Springer
ISBN 13 : 9811025940
Total Pages : 222 pages
Book Rating : 4.8/5 (11 download)

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Book Synopsis Advanced Statistical Methods in Data Science by : Ding-Geng Chen

Download or read book Advanced Statistical Methods in Data Science written by Ding-Geng Chen and published by Springer. This book was released on 2016-11-30 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers invited presentations from the 2nd Symposium of the ICSA- CANADA Chapter held at the University of Calgary from August 4-6, 2015. The aim of this Symposium was to promote advanced statistical methods in big-data sciences and to allow researchers to exchange ideas on statistics and data science and to embraces the challenges and opportunities of statistics and data science in the modern world. It addresses diverse themes in advanced statistical analysis in big-data sciences, including methods for administrative data analysis, survival data analysis, missing data analysis, high-dimensional and genetic data analysis, longitudinal and functional data analysis, the design and analysis of studies with response-dependent and multi-phase designs, time series and robust statistics, statistical inference based on likelihood, empirical likelihood and estimating functions. The editorial group selected 14 high-quality presentations from this successful symposium and invited the presenters to prepare a full chapter for this book in order to disseminate the findings and promote further research collaborations in this area. This timely book offers new methods that impact advanced statistical model development in big-data sciences.

Recent Advances in Statistical Research and Data Analysis

Author : Y. Baba
Publisher : Springer Science & Business Media
ISBN 13 : 4431685448
Total Pages : 133 pages
Book Rating : 4.4/5 (316 download)

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Book Synopsis Recent Advances in Statistical Research and Data Analysis by : Y. Baba

Download or read book Recent Advances in Statistical Research and Data Analysis written by Y. Baba and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent Advances in Statistical Research and Data Analysis is a collection of papers presented at the symposium of the same name, held in Tokyo by the Center for Information on Statistical Science of the Institute of Statistical Mathematics (ISM). Under the auspices of the Ministry of Education, Culture, Sports, Science and Technology of Japan, the ISM has created visiting professorships and organized symposia to promote collaboration between researchers from Japan and those from other countries. At the symposium on recent advances in statistical research and data analysis, the keynote speaker was Visiting Professor Anthony J. Hayter. This book includes Prof. Hayter's address as well as papers from special lectures that were presented at the symposium. All the contributions are concerned with theory and methodology for real data and thus will benefit researchers, students, and others engaged in data analysis.

Principles and Methods for Data Science

Author :
Publisher : Elsevier
ISBN 13 : 0444642129
Total Pages : 498 pages
Book Rating : 4.4/5 (446 download)

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Book Synopsis Principles and Methods for Data Science by :

Download or read book Principles and Methods for Data Science written by and published by Elsevier. This book was released on 2020-05-28 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principles and Methods for Data Science, Volume 43 in the Handbook of Statistics series, highlights new advances in the field, with this updated volume presenting interesting and timely topics, including Competing risks, aims and methods, Data analysis and mining of microbial community dynamics, Support Vector Machines, a robust prediction method with applications in bioinformatics, Bayesian Model Selection for Data with High Dimension, High dimensional statistical inference: theoretical development to data analytics, Big data challenges in genomics, Analysis of microarray gene expression data using information theory and stochastic algorithm, Hybrid Models, Markov Chain Monte Carlo Methods: Theory and Practice, and more. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Handbook of Statistics series Updated release includes the latest information on Principles and Methods for Data Science

Data Science

Author : Francesco Palumbo
Publisher : Springer
ISBN 13 : 3319557238
Total Pages : 342 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Data Science by : Francesco Palumbo

Download or read book Data Science written by Francesco Palumbo and published by Springer. This book was released on 2017-07-04 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume on the latest advances in data science covers a wide range of topics in the context of data analysis and classification. In particular, it includes contributions on classification methods for high-dimensional data, clustering methods, multivariate statistical methods, and various applications. The book gathers a selection of peer-reviewed contributions presented at the Fifteenth Conference of the International Federation of Classification Societies (IFCS2015), which was hosted by the Alma Mater Studiorum, University of Bologna, from July 5 to 8, 2015.

Advances and Innovations in Statistics and Data Science

Author : Wenqing He
Publisher : Springer
ISBN 13 : 9783031083310
Total Pages : 0 pages
Book Rating : 4.0/5 (833 download)

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Book Synopsis Advances and Innovations in Statistics and Data Science by : Wenqing He

Download or read book Advances and Innovations in Statistics and Data Science written by Wenqing He and published by Springer. This book was released on 2023-11-11 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights selected papers from the 4th ICSA-Canada Chapter Symposium, as well as invited articles from established researchers in the areas of statistics and data science. It covers a variety of topics, including methodology development in data science, such as methodology in the analysis of high dimensional data, feature screening in ultra-high dimensional data and natural language ranking; statistical analysis challenges in sampling, multivariate survival models and contaminated data, as well as applications of statistical methods. With this book, readers can make use of frontier research methods to tackle their problems in research, education, training and consultation.

Statistics for Data Science

Author : James D. Miller
Publisher : Packt Publishing Ltd
ISBN 13 : 178829534X
Total Pages : 279 pages
Book Rating : 4.7/5 (882 download)

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Book Synopsis Statistics for Data Science by : James D. Miller

Download or read book Statistics for Data Science written by James D. Miller and published by Packt Publishing Ltd. This book was released on 2017-11-17 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get your statistics basics right before diving into the world of data science About This Book No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs; Implement statistics in data science tasks such as data cleaning, mining, and analysis Learn all about probability, statistics, numerical computations, and more with the help of R programs Who This Book Is For This book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful. What You Will Learn Analyze the transition from a data developer to a data scientist mindset Get acquainted with the R programs and the logic used for statistical computations Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks Get comfortable with performing various statistical computations for data science programmatically In Detail Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on. This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks. By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically. Style and approach Step by step comprehensive guide with real world examples

Advanced Statistical Methods for the Analysis of Large Data-Sets

Author : Agostino Di Ciaccio
Publisher : Springer Science & Business Media
ISBN 13 : 3642210376
Total Pages : 464 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Advanced Statistical Methods for the Analysis of Large Data-Sets by : Agostino Di Ciaccio

Download or read book Advanced Statistical Methods for the Analysis of Large Data-Sets written by Agostino Di Ciaccio and published by Springer Science & Business Media. This book was released on 2012-03-05 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: The theme of the meeting was “Statistical Methods for the Analysis of Large Data-Sets”. In recent years there has been increasing interest in this subject; in fact a huge quantity of information is often available but standard statistical techniques are usually not well suited to managing this kind of data. The conference serves as an important meeting point for European researchers working on this topic and a number of European statistical societies participated in the organization of the event. The book includes 45 papers from a selection of the 156 papers accepted for presentation and discussed at the conference on “Advanced Statistical Methods for the Analysis of Large Data-sets.”

New Statistical Developments in Data Science

Author : Alessandra Petrucci
Publisher : Springer Nature
ISBN 13 : 3030211584
Total Pages : 479 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis New Statistical Developments in Data Science by : Alessandra Petrucci

Download or read book New Statistical Developments in Data Science written by Alessandra Petrucci and published by Springer Nature. This book was released on 2019-08-20 with total page 479 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

New Statistical Developments in Data Science

Author : Società italiana di statistica. Riunione scientifica
Publisher :
ISBN 13 : 9783030211592
Total Pages : pages
Book Rating : 4.2/5 (115 download)

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Book Synopsis New Statistical Developments in Data Science by : Società italiana di statistica. Riunione scientifica

Download or read book New Statistical Developments in Data Science written by Società italiana di statistica. Riunione scientifica and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Computer Age Statistical Inference

Author : Bradley Efron
Publisher : Cambridge University Press
ISBN 13 : 1108107958
Total Pages : 496 pages
Book Rating : 4.1/5 (81 download)

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Book Synopsis Computer Age Statistical Inference by : Bradley Efron

Download or read book Computer Age Statistical Inference written by Bradley Efron and published by Cambridge University Press. This book was released on 2016-07-21 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

New Developments in Statistical Information Theory Based on Entropy and Divergence Measures

Author : Leandro Pardo
Publisher : MDPI
ISBN 13 : 3038979368
Total Pages : 344 pages
Book Rating : 4.0/5 (389 download)

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Book Synopsis New Developments in Statistical Information Theory Based on Entropy and Divergence Measures by : Leandro Pardo

Download or read book New Developments in Statistical Information Theory Based on Entropy and Divergence Measures written by Leandro Pardo and published by MDPI. This book was released on 2019-05-20 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents new and original research in Statistical Information Theory, based on minimum divergence estimators and test statistics, from a theoretical and applied point of view, for different statistical problems with special emphasis on efficiency and robustness. Divergence statistics, based on maximum likelihood estimators, as well as Wald’s statistics, likelihood ratio statistics and Rao’s score statistics, share several optimum asymptotic properties, but are highly non-robust in cases of model misspecification under the presence of outlying observations. It is well-known that a small deviation from the underlying assumptions on the model can have drastic effect on the performance of these classical tests. Specifically, this book presents a robust version of the classical Wald statistical test, for testing simple and composite null hypotheses for general parametric models, based on minimum divergence estimators.

Data Science Concepts and Techniques with Applications

Author : Usman Qamar
Publisher : Springer Nature
ISBN 13 : 9811561338
Total Pages : 207 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Data Science Concepts and Techniques with Applications by : Usman Qamar

Download or read book Data Science Concepts and Techniques with Applications written by Usman Qamar and published by Springer Nature. This book was released on 2020-06-08 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprehensively covers the topic of data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three sections: The first section is an introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics. Followed by discussion on wide range of applications of data science and widely used techniques in data science. The second section is devoted to the tools and techniques of data science. It consists of data pre-processing, feature selection, classification and clustering concepts as well as an introduction to text mining and opining mining. And finally, the third section of the book focuses on two programming languages commonly used for data science projects i.e. Python and R programming language. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. The book is suitable for both undergraduate and postgraduate students as well as those carrying out research in data science. It can be used as a textbook for undergraduate students in computer science, engineering and mathematics. It can also be accessible to undergraduate students from other areas with the adequate background. The more advanced chapters can be used by postgraduate researchers intending to gather a deeper theoretical understanding.

Advanced Studies in Classification and Data Science

Author : Tadashi Imaizumi
Publisher : Springer
ISBN 13 : 9789811533105
Total Pages : 524 pages
Book Rating : 4.5/5 (331 download)

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Book Synopsis Advanced Studies in Classification and Data Science by : Tadashi Imaizumi

Download or read book Advanced Studies in Classification and Data Science written by Tadashi Imaizumi and published by Springer. This book was released on 2020-09-26 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Trends in Mathematical, Information and Data Sciences

Author : Narayanaswamy Balakrishnan
Publisher : Springer Nature
ISBN 13 : 3031041372
Total Pages : 450 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Trends in Mathematical, Information and Data Sciences by : Narayanaswamy Balakrishnan

Download or read book Trends in Mathematical, Information and Data Sciences written by Narayanaswamy Balakrishnan and published by Springer Nature. This book was released on 2022-06-27 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book involves ideas/results from the topics of mathematical, information, and data sciences, in connection with the main research interests of Professor Pardo that can be summarized as Information Theory with Applications to Statistical Inference. This book is a tribute to Professor Leandro Pardo, who has chaired the Department of Statistics and OR of the Complutense University in Madrid, and he has been also President of the Spanish Society of Statistics and Operations Research. In this way, the contributions have been structured into three parts, which often overlap to a greater or lesser extent, namely Trends in Mathematical Sciences (Part I) Trends in Information Sciences (Part II) Trends in Data Sciences (Part III) The contributions gathered in this book have offered either new developments from a theoretical and/or computational and/or applied point of view, or reviews of recent literature of outstanding developments. They have been applied through nice examples in climatology, chemistry, economics, engineering, geology, health sciences, physics, pandemics, and socioeconomic indicators. Consequently, the intended audience of this book is mainly statisticians, mathematicians, computer scientists, and so on, but users of these disciplines as well as experts in the involved applications may certainly find this book a very interesting read.

Statistical Data Analytics

Author : Walter W. Piegorsch
Publisher : John Wiley & Sons
ISBN 13 : 1119043573
Total Pages : 488 pages
Book Rating : 4.1/5 (19 download)

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Book Synopsis Statistical Data Analytics by : Walter W. Piegorsch

Download or read book Statistical Data Analytics written by Walter W. Piegorsch and published by John Wiley & Sons. This book was released on 2015-06-11 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Data Science and Social Research II

Author : Paolo Mariani
Publisher : Springer Nature
ISBN 13 : 3030512223
Total Pages : 391 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Data Science and Social Research II by : Paolo Mariani

Download or read book Data Science and Social Research II written by Paolo Mariani and published by Springer Nature. This book was released on 2020-11-25 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: The peer-reviewed contributions gathered in this book address methods, software and applications of statistics and data science in the social sciences. The data revolution in social science research has not only produced new business models, but has also provided policymakers with better decision-making support tools. In this volume, statisticians, computer scientists and experts on social research discuss the opportunities and challenges of the social data revolution in order to pave the way for addressing new research problems. The respective contributions focus on complex social systems and current methodological advances in extracting social knowledge from large data sets, as well as modern social research on human behavior and society using large data sets. Moreover, they analyze integrated systems designed to take advantage of new social data sources, and discuss quality-related issues. The papers were originally presented at the 2nd International Conference on Data Science and Social Research, held in Milan, Italy, on February 4-5, 2019.