MULTIVARIATE STATISTICS OLD SCHOOL MATHEMATICAL AND METHODOLOGICAL INTRODUCTION TO MULTIVARIATE STATISTICAL ANALYTICS INCLUDING LINEAR MODELS MACHINE LEARNING AND BIG DATA STUDY WITH R

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Multivariate Statistics Old School

Author : John I. Marden
ISBN : 1456538837
Genre :
File Size : 23.92 MB
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Multivariate Statistics: Old School is amathematical and methodological introduction to multivariate statistical analysis. It presents the basic mathematical grounding that graduate statistics students need for future research, andimportant multivariate techniques useful to statisticians in general. The material provides support forfurther study in big data and machine learning. Topics include The multivariate normal and Wishart distributions Linear models, including multivariate regression and analysis of variance, andboth-sides models (GMANOVA, repeated measures, growth curves) Linear algebra useful for multivariate statistics Covariance structures, including principal components, factor analysis, independence and conditional independence, and symmetry models Classification (linear and quadratic discrimination, trees, logistic regression) Clustering (K-means, model-based, hierarchical) Other techniques, including biplots, canonical correlations, and multidimensional scaling Most of the analyses in the book use the statistical computing environment R, for which there is an available package (msos)of multivariate routines and data sets. This text was developed over many years by the author, John Marden, while teaching in the Department of Statistics, University of Illinoisat Urbana-Champaign.
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Mathematical Statistics

Author : John I. Marden
ISBN : 1542439604
Genre :
File Size : 58.38 MB
Format : PDF
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Mathematical Statistics: Old School covers three main areas: The mathematics needed as a basisfor work in statistics; the mathematical methods for carrying out statistical inference; and the theoretical approaches for analyzing the efficacy of various procedures. The author, John Marden,developed this material over the last thirty years teaching various configurations of mathematical statisticsand decision theory courses. It is intended as a graduate-level text. Topics include distribution theory, asymptotic convergence, frequentist and Bayesian inference (estimation, hypothesis testing, confidence intervals, model selection), exponential families, linear regression, likelihood methods, bootstrap and randomization methods, and statistical decision theory.
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An Introduction To Statistical Learning

Author : Gareth James
ISBN : 9781461471387
Genre : Mathematics
File Size : 79.85 MB
Format : PDF, ePub
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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
Category: Mathematics

The R Book

Author : Michael J. Crawley
ISBN : 9781118448960
Genre : Mathematics
File Size : 59.93 MB
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Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: Features full colour text and extensive graphics throughout. Introduces a clear structure with numbered section headings to help readers locate information more efficiently. Looks at the evolution of R over the past five years. Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully revised and updated bibliography and reference section. Is supported by an accompanying website allowing examples from the text to be run by the user. Praise for the first edition: ‘…if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.’ (The American Statistician, August 2008) ‘The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book…’ (Professional Pensions, July 2007)
Category: Mathematics

Harness Oil And Gas Big Data With Analytics

Author : Keith Holdaway
ISBN : 9781118910894
Genre : Business & Economics
File Size : 27.90 MB
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Use big data analytics to efficiently drive oil and gas exploration and production Harness Oil and Gas Big Data with Analytics provides a complete view of big data and analytics techniques as they are applied to the oil and gas industry. Including a compendium of specific case studies, the book underscores the acute need for optimization in the oil and gas exploration and production stages and shows how data analytics can provide such optimization. This spans exploration, development, production and rejuvenation of oil and gas assets. The book serves as a guide for fully leveraging data, statistical, and quantitative analysis, exploratory and predictive modeling, and fact-based management to drive decision making in oil and gas operations. This comprehensive resource delves into the three major issues that face the oil and gas industry during the exploration and production stages: Data management, including storing massive quantities of data in a manner conducive to analysis and effectively retrieving, backing up, and purging data Quantification of uncertainty, including a look at the statistical and data analytics methods for making predictions and determining the certainty of those predictions Risk assessment, including predictive analysis of the likelihood that known risks are realized and how to properly deal with unknown risks Covering the major issues facing the oil and gas industry in the exploration and production stages, Harness Big Data with Analytics reveals how to model big data to realize efficiencies and business benefits.
Category: Business & Economics

Machine Learning Algorithms

Author : Giuseppe Bonaccorso
ISBN : 9781785884511
Genre : Computers
File Size : 87.66 MB
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Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn Acquaint yourself with important elements of Machine Learning Understand the feature selection and feature engineering process Assess performance and error trade-offs for Linear Regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector machines Implement clusters to a dataset Explore the concept of Natural Processing Language and Recommendation Systems Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.
Category: Computers

Modern Multivariate Statistical Techniques

Author : Alan J. Izenman
ISBN : 0387781897
Genre : Mathematics
File Size : 23.89 MB
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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.
Category: Mathematics

Understanding Machine Learning

Author : Shai Shalev-Shwartz
ISBN : 9781107057135
Genre : Computers
File Size : 35.24 MB
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Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Category: Computers

Statistical Regression And Classification

Author : Norman Matloff
ISBN : 9781351645898
Genre : Business & Economics
File Size : 89.41 MB
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Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough treatment of classical linear and generalized linear models, supplemented with introductory material on machine learning methods. * Since classification is the focus of many contemporary applications, the book covers this topic in detail, especially the multiclass case. * In view of the voluminous nature of many modern datasets, there is a chapter on Big Data. * Has special Mathematical and Computational Complements sections at ends of chapters, and exercises are partitioned into Data, Math and Complements problems. * Instructors can tailor coverage for specific audiences such as majors in Statistics, Computer Science, or Economics. * More than 75 examples using real data. The book treats classical regression methods in an innovative, contemporary manner. Though some statistical learning methods are introduced, the primary methodology used is linear and generalized linear parametric models, covering both the Description and Prediction goals of regression methods. The author is just as interested in Description applications of regression, such as measuring the gender wage gap in Silicon Valley, as in forecasting tomorrow's demand for bike rentals. An entire chapter is devoted to measuring such effects, including discussion of Simpson's Paradox, multiple inference, and causation issues. Similarly, there is an entire chapter of parametric model fit, making use of both residual analysis and assessment via nonparametric analysis. Norman Matloff is a professor of computer science at the University of California, Davis, and was a founder of the Statistics Department at that institution. His current research focus is on recommender systems, and applications of regression methods to small area estimation and bias reduction in observational studies. He is on the editorial boards of the Journal of Statistical Computation and the R Journal. An award-winning teacher, he is the author of The Art of R Programming and Parallel Computation in Data Science: With Examples in R, C++ and CUDA.
Category: Business & Economics

Multivariate Analysis Of Ecological Data

Author : Michael Greenacre
ISBN : 9788492937509
Genre : Ecology
File Size : 36.18 MB
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La diversidad biológica es fruto de la interacción entre numerosas especies, ya sean marinas, vegetales o animales, a la par que de los muchos factores limitantes que caracterizan el medio que habitan. El análisis multivariante utiliza las relaciones entre diferentes variables para ordenar los objetos de estudio según sus propiedades colectivas y luego clasificarlos; es decir, agrupar especies o ecosistemas en distintas clases compuestas cada una por entidades con propiedades parecidas. El fin último es relacionar la variabilidad biológica observada con las correspondientes características medioambientales. Multivariate Analysis of Ecological Data explica de manera completa y estructurada cómo analizar e interpretar los datos ecológicos observados sobre múltiples variables, tanto biológicos como medioambientales. Tras una introducción general a los datos ecológicos multivariantes y la metodología estadística, se abordan en capítulos específicos, métodos como aglomeración (clustering), regresión, biplots, escalado multidimensional, análisis de correspondencias (simple y canónico) y análisis log-ratio, con atención también a sus problemas de modelado y aspectos inferenciales. El libro plantea una serie de aplicaciones a datos reales derivados de investigaciones ecológicas, además de dos casos detallados que llevan al lector a apreciar los retos de análisis, interpretación y comunicación inherentes a los estudios a gran escala y los diseños complejos.
Category: Ecology