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Exploratory Data Analysis with MATLAB

Exploratory Data Analysis with MATLAB Author Wendy L. Martinez
ISBN-10 149877606X
Release 2017-06
Pages 590
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Praise for the Second Edition: "The authors present an intuitive and easy-to-read book. ... accompanied by many examples, proposed exercises, good references, and comprehensive appendices that initiate the reader unfamiliar with MATLAB." --Adolfo Alvarez Pinto, International Statistical Review "Practitioners of EDA who use MATLAB will want a copy of this book. ... The authors have done a great service by bringing together so many EDA routines, but their main accomplishment in this dynamic text is providing the understanding and tools to do EDA. --David A Huckaby, MAA Reviews Exploratory Data Analysis (EDA) is an important part of the data analysis process. The methods presented in this text are ones that should be in the toolkit of every data scientist. As computational sophistication has increased and data sets have grown in size and complexity, EDA has become an even more important process for visualizing and summarizing data before making assumptions to generate hypotheses and models. Exploratory Data Analysis with MATLAB, Third Edition presents EDA methods from a computational perspective and uses numerous examples and applications to show how the methods are used in practice. The authors use MATLAB code, pseudo-code, and algorithm descriptions to illustrate the concepts. The MATLAB code for examples, data sets, and the EDA Toolbox are available for download on the book's website. New to the Third Edition Random projections and estimating local intrinsic dimensionality Deep learning autoencoders and stochastic neighbor embedding Minimum spanning tree and additional cluster validity indices Kernel density estimation Plots for visualizing data distributions, such as beanplots and violin plots A chapter on visualizing categorical data



Computational Statistics Handbook with MATLAB Third Edition

Computational Statistics Handbook with MATLAB  Third Edition Author Wendy L. Martinez
ISBN-10 9781466592742
Release 2015-12-16
Pages 731
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A Strong Practical Focus on Applications and Algorithms Computational Statistics Handbook with MATLAB®, Third Edition covers today’s most commonly used techniques in computational statistics while maintaining the same philosophy and writing style of the bestselling previous editions. The text keeps theoretical concepts to a minimum, emphasizing the implementation of the methods. New to the Third Edition This third edition is updated with the latest version of MATLAB and the corresponding version of the Statistics and Machine Learning Toolbox. It also incorporates new sections on the nearest neighbor classifier, support vector machines, model checking and regularization, partial least squares regression, and multivariate adaptive regression splines. Web Resource The authors include algorithmic descriptions of the procedures as well as examples that illustrate the use of algorithms in data analysis. The MATLAB code, examples, and data sets are available online.



Resampling Methods

Resampling Methods Author Phillip I. Good
ISBN-10 0817643869
Release 2005-09-08
Pages 218
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This thoroughly revised and expanded third edition is a practical guide to data analysis using the bootstrap, cross-validation, and permutation tests. Only requiring minimal mathematics beyond algebra, it provides a table-free introduction to data analysis utilizing numerous exercises, practical data sets, and freely available statistical shareware. New to the third edition are additional program listings and screen shots of C++, CART, Blossom, Box Sampler (an Excel add-in), EViews, MATLAB, R, Resampling Stats, SAS macros, S-Plus, Stata, or StatXact, which accompany each resampling procedure. A glossary and solutions to selected exercises have also been added. With its accessible style and intuitive topic development, the book is an excellent basic resource for the power, simplicity, and versatility of resampling methods. It is an essential resource for statisticians, biostatisticians, statistical consultants, students, and research professionals in the biological, physical, and social sciences, engineering, and technology.



Modern Multivariate Statistical Techniques

Modern Multivariate Statistical Techniques Author Alan J. Izenman
ISBN-10 0387781897
Release 2009-03-02
Pages 733
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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.



Computational and Statistical Methods for Analysing Big Data with Applications

Computational and Statistical Methods for Analysing Big Data with Applications Author Shen Liu
ISBN-10 9780081006511
Release 2015-11-20
Pages 206
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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



The Top Ten Algorithms in Data Mining

The Top Ten Algorithms in Data Mining Author Xindong Wu
ISBN-10 142008965X
Release 2009-04-09
Pages 208
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Identifying some of the most influential algorithms that are widely used in the data mining community, The Top Ten Algorithms in Data Mining provides a description of each algorithm, discusses its impact, and reviews current and future research. Thoroughly evaluated by independent reviewers, each chapter focuses on a particular algorithm and is written by either the original authors of the algorithm or world-class researchers who have extensively studied the respective algorithm. The book concentrates on the following important algorithms: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. Examples illustrate how each algorithm works and highlight its overall performance in a real-world application. The text covers key topics—including classification, clustering, statistical learning, association analysis, and link mining—in data mining research and development as well as in data mining, machine learning, and artificial intelligence courses. By naming the leading algorithms in this field, this book encourages the use of data mining techniques in a broader realm of real-world applications. It should inspire more data mining researchers to further explore the impact and novel research issues of these algorithms.



Climate Time Series Analysis

Climate Time Series Analysis Author Manfred Mudelsee
ISBN-10 9048194822
Release 2010-08-26
Pages 474
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Climate is a paradigm of a complex system. Analysing climate data is an exciting challenge, which is increased by non-normal distributional shape, serial dependence, uneven spacing and timescale uncertainties. This book presents bootstrap resampling as a computing-intensive method able to meet the challenge. It shows the bootstrap to perform reliably in the most important statistical estimation techniques: regression, spectral analysis, extreme values and correlation. This book is written for climatologists and applied statisticians. It explains step by step the bootstrap algorithms (including novel adaptions) and methods for confidence interval construction. It tests the accuracy of the algorithms by means of Monte Carlo experiments. It analyses a large array of climate time series, giving a detailed account on the data and the associated climatological questions. This makes the book self-contained for graduate students and researchers.



Biostatistics with R

Biostatistics with R Author Babak Shahbaba
ISBN-10 9781461413028
Release 2011-12-15
Pages 352
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Biostatistics with R is designed around the dynamic interplay among statistical methods, their applications in biology, and their implementation. The book explains basic statistical concepts with a simple yet rigorous language. The development of ideas is in the context of real applied problems, for which step-by-step instructions for using R and R-Commander are provided. Topics include data exploration, estimation, hypothesis testing, linear regression analysis, and clustering with two appendices on installing and using R and R-Commander. A novel feature of this book is an introduction to Bayesian analysis. This author discusses basic statistical analysis through a series of biological examples using R and R-Commander as computational tools. The book is ideal for instructors of basic statistics for biologists and other health scientists. The step-by-step application of statistical methods discussed in this book allows readers, who are interested in statistics and its application in biology, to use the book as a self-learning text.



Clustering for Data Mining

Clustering for Data Mining Author Boris Mirkin
ISBN-10 9781420034912
Release 2005-04-29
Pages 296
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Often considered more as an art than a science, the field of clustering has been dominated by learning through examples and by techniques chosen almost through trial-and-error. Even the most popular clustering methods--K-Means for partitioning the data set and Ward's method for hierarchical clustering--have lacked the theoretical attention that would establish a firm relationship between the two methods and relevant interpretation aids. Rather than the traditional set of ad hoc techniques, Clustering for Data Mining: A Data Recovery Approach presents a theory that not only closes gaps in K-Means and Ward methods, but also extends them into areas of current interest, such as clustering mixed scale data and incomplete clustering. The author suggests original methods for both cluster finding and cluster description, addresses related topics such as principal component analysis, contingency measures, and data visualization, and includes nearly 60 computational examples covering all stages of clustering, from data pre-processing to cluster validation and results interpretation. This author's unique attention to data recovery methods, theory-based advice, pre- and post-processing issues that are beyond the scope of most texts, and clear, practical instructions for real-world data mining make this book ideally suited for virtually all purposes: for teaching, for self-study, and for professional reference.



Statistical and Computational Methods in Brain Image Analysis

Statistical and Computational Methods in Brain Image Analysis Author Moo K. Chung
ISBN-10 9781439836354
Release 2013-07-23
Pages 400
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The massive amount of nonstandard high-dimensional brain imaging data being generated is often difficult to analyze using current techniques. This challenge in brain image analysis requires new computational approaches and solutions. But none of the research papers or books in the field describe the quantitative techniques with detailed illustrations of actual imaging data and computer codes. Using MATLAB® and case study data sets, Statistical and Computational Methods in Brain Image Analysis is the first book to explicitly explain how to perform statistical analysis on brain imaging data. The book focuses on methodological issues in analyzing structural brain imaging modalities such as MRI and DTI. Real imaging applications and examples elucidate the concepts and methods. In addition, most of the brain imaging data sets and MATLAB codes are available on the author’s website. By supplying the data and codes, this book enables researchers to start their statistical analyses immediately. Also suitable for graduate students, it provides an understanding of the various statistical and computational methodologies used in the field as well as important and technically challenging topics.



Big Data Management and Processing

Big Data Management and Processing Author Kuan-Ching Li
ISBN-10 9781351650045
Release 2017-05-19
Pages 469
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From the Foreword: "Big Data Management and Processing is [a] state-of-the-art book that deals with a wide range of topical themes in the field of Big Data. The book, which probes many issues related to this exciting and rapidly growing field, covers processing, management, analytics, and applications... [It] is a very valuable addition to the literature. It will serve as a source of up-to-date research in this continuously developing area. The book also provides an opportunity for researchers to explore the use of advanced computing technologies and their impact on enhancing our capabilities to conduct more sophisticated studies." ---Sartaj Sahni, University of Florida, USA "Big Data Management and Processing covers the latest Big Data research results in processing, analytics, management and applications. Both fundamental insights and representative applications are provided. This book is a timely and valuable resource for students, researchers and seasoned practitioners in Big Data fields. --Hai Jin, Huazhong University of Science and Technology, China Big Data Management and Processing explores a range of big data related issues and their impact on the design of new computing systems. The twenty-one chapters were carefully selected and feature contributions from several outstanding researchers. The book endeavors to strike a balance between theoretical and practical coverage of innovative problem solving techniques for a range of platforms. It serves as a repository of paradigms, technologies, and applications that target different facets of big data computing systems. The first part of the book explores energy and resource management issues, as well as legal compliance and quality management for Big Data. It covers In-Memory computing and In-Memory data grids, as well as co-scheduling for high performance computing applications. The second part of the book includes comprehensive coverage of Hadoop and Spark, along with security, privacy, and trust challenges and solutions. The latter part of the book covers mining and clustering in Big Data, and includes applications in genomics, hospital big data processing, and vehicular cloud computing. The book also analyzes funding for Big Data projects.



Advances in Intelligent Data Analysis VIII

Advances in Intelligent Data Analysis VIII Author Niall M. Adams
ISBN-10 9783642039157
Release 2009-08-27
Pages 418
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Advances in Intelligent Data Analysis VIII has been writing in one form or another for most of life. You can find so many inspiration from Advances in Intelligent Data Analysis VIII also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Advances in Intelligent Data Analysis VIII book for free.



Advances in Digital Forensics IV

Advances in Digital Forensics IV Author Indrajit Ray
ISBN-10 9780387849263
Release 2008-08-28
Pages 366
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Practically every crime now involves some aspect of digital evidence. This is the most recent volume in the Advances in Digital Forensics series. It describes original research results and innovative applications in the emerging discipline of digital forensics. In addition, it highlights some of the major technical and legal issues related to digital evidence and electronic crime investigations. This book contains a selection of twenty-eight edited papers from the Fourth Annual IFIP WG 11.9 Conference on Digital Forensics, held at Kyoto University, Kyoto, Japan in the spring of 2008.



Actuarial Sciences and Quantitative Finance

Actuarial Sciences and Quantitative Finance Author Jaime A. Londoño
ISBN-10 9783319665368
Release 2017-10-24
Pages 174
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Developed from the Second International Congress on Actuarial Science and Quantitative Finance, this volume showcases the latest progress in all theoretical and empirical aspects of actuarial science and quantitative finance. Held at the Universidad de Cartagena in Cartegena, Colombia in June 2016, the conference emphasized relations between industry and academia and provided a platform for practitioners to discuss problems arising from the financial and insurance industries in the Andean and Caribbean regions. Based on invited lectures as well as carefully selected papers, these proceedings address topics such as statistical techniques in finance and actuarial science, portfolio management, risk theory, derivative valuation and economics of insurance.



Smart Water Grids

Smart Water Grids Author Panagiotis Tsakalides
ISBN-10 9781351986175
Release 2018-04-17
Pages 348
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This book will present the best practices for designing, implementing, and deploying cyber-physical systems tailored to the needs of smart water grids. These grids can utilize the intelligence, autonomy, and adaptability offered by CPS for data on consumption, new alternatives for water treatment and reusability, and the impacts of climate change on water sources and urban infrastructure. It will examine topics such as smart sensing, distributed processing, networked control, enabling technologies, and heterogeneous networked topologies, and will also include case studies which will cover different aspects of the water life cycle, such as desalination, distribution, treatment, and recycling.



Revisiting the Effectiveness of Transcranial Direct Current Brain Stimulation for Cognition Evidence Challenges and Open Questions

Revisiting the Effectiveness of Transcranial Direct Current Brain Stimulation for Cognition  Evidence  Challenges  and Open Questions Author Evangelia G. Chrysikou
ISBN-10 9782889453252
Release 2017-11-15
Pages
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The aim of this Frontiers Research Topic is to assemble a collection of papers from experts in the field of non‐invasive brain stimulation that will discuss (1) the strength of the evidence regarding the potential of tDCS to modulate different aspects of cognition; (2) methodological caveats associated with the technique that may account for the variability in the reported findings; and (3) a set of challenges and future directions for the use of tDCS that can determine its potential as a reliable method for cognitive rehabilitation, maintenance, or enhancement.



Statistical Analysis Techniques in Particle Physics

Statistical Analysis Techniques in Particle Physics Author Ilya Narsky
ISBN-10 9783527677290
Release 2013-10-24
Pages 400
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Modern analysis of HEP data needs advanced statistical tools to separate signal from background. This is the first book which focuses on machine learning techniques. It will be of interest to almost every high energy physicist, and, due to its coverage, suitable for students.