Statistics: A Gentle Introduction, Coolidge Frederick L.
Старое издание
Автор: Guenin Название: A Gentle Introduction to Optimization ISBN: 1107658799 ISBN-13(EAN): 9781107658790 Издательство: Cambridge Academ Рейтинг: Цена: 43290.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Assuming only basic linear algebra, this textbook is the perfect starting point for a wide range of undergraduate students from across the mathematical sciences (computer science, engineering, economics, and so on). The authors focus on the fundamental ideas in optimization and motivate the theory with real-world examples and exercises.
Автор: Taylor, Steven J. Bogdan, Robert DeVault, Marjorie Название: Introduction To Qualitative Research Methods ISBN: 1118767217 ISBN-13(EAN): 9781118767214 Издательство: Wiley Рейтинг: Цена: 68590.00 T Наличие на складе: Поставка под заказ. Описание: An informative real-world guide to studying the "why" of human behavior Introduction to Qualitative Research Methods is a practical, comprehensive guide to the collection and presentation of qualitative data.
Автор: Joseph K. Blitzstein, Jessica Hwang Название: Introduction to Probability, Second Edition ISBN: 1138369918 ISBN-13(EAN): 9781138369917 Издательство: Taylor&Francis Рейтинг: Цена: 74510.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Assumes one-semester of calculus. "Stories" make distributions (Normal, Binomial, Poisson that are widely-used in statistics) easier to remember, understand. Many books write down formulas without explaining clearly why these particular distributions are important or how they are all connected.
Автор: James Gareth Название: An Introduction to Statistical Learning ISBN: 1461471370 ISBN-13(EAN): 9781461471370 Издательство: Springer Рейтинг: Цена: 60550.00 T Наличие на складе: Невозможна поставка. Описание: This book presents key modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering.
A brand new, fully updated edition of a popular classic on matrix differential calculus with applications in statistics and econometrics
This exhaustive, self-contained book on matrix theory and matrix differential calculus provides a treatment of matrix calculus based on differentials and shows how easy it is to use this theory once you have mastered the technique. Jan Magnus, who, along with the late Heinz Neudecker, pioneered the theory, develops it further in this new edition and provides many examples along the way to support it.
Matrix calculus has become an essential tool for quantitative methods in a large number of applications, ranging from social and behavioral sciences to econometrics. It is still relevant and used today in a wide range of subjects such as the biosciences and psychology. Matrix Differential Calculus with Applications in Statistics and Econometrics, Third Edition contains all of the essentials of multivariable calculus with an emphasis on the use of differentials. It starts by presenting a concise, yet thorough overview of matrix algebra, then goes on to develop the theory of differentials. The rest of the text combines the theory and application of matrix differential calculus, providing the practitioner and researcher with both a quick review and a detailed reference.
Fulfills the need for an updated and unified treatment of matrix differential calculus
Contains many new examples and exercises based on questions asked of the author over the years
Covers new developments in field and features new applications
Written by a leading expert and pioneer of the theory
Part of the Wiley Series in Probability and Statistics
Matrix Differential Calculus With Applications in Statistics and Econometrics Third Edition is an ideal text for graduate students and academics studying the subject, as well as for postgraduates and specialists working in biosciences and psychology.
Автор: Joshi Название: Introduction to Mathematical Portfolio Theory ISBN: 1107042313 ISBN-13(EAN): 9781107042315 Издательство: Cambridge Academ Рейтинг: Цена: 60190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: A concise yet comprehensive guide to the mathematics of portfolio theory from a modelling perspective, with discussion of the assumptions, limitations and implementations of the models as well as the theory underlying them. Aimed at advanced undergraduates, this book can be used for self-study or as a course text.
A practical guide to using modern software effectively in quantitative research in the social and natural sciences.
This book offers a practical guide to the computational methods at the heart of most modern quantitative research. It will be essential reading for research assistants needing hands-on experience; students entering PhD programs in business, economics, and other social or natural sciences; and those seeking quantitative jobs in industry. No background in computer science is assumed; a learner need only have a computer with access to the Internet. Using the example as its principal pedagogical device, the book offers tried-and-true prototypes that illustrate many important computational tasks required in quantitative research. The best way to use the book is to read it at the computer keyboard and learn by doing.
The book begins by introducing basic skills: how to use the operating system, how to organize data, and how to complete simple programming tasks. For its demonstrations, the book uses a UNIX-based operating system and a set of free software tools: the scripting language Python for programming tasks; the database management system SQLite; and the freely available R for statistical computing and graphics. The book goes on to describe particular tasks: analyzing data, implementing commonly used numerical and simulation methods, and creating extensions to Python to reduce cycle time. Finally, the book describes the use of LaTeX, a document markup language and preparation system.
Автор: Bradley Efron and Trevor Hastie Название: Computer Age Statistical Inference ISBN: 1107149894 ISBN-13(EAN): 9781107149892 Издательство: Cambridge Academ Рейтинг: Цена: 60190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
Автор: Guenin Название: A Gentle Introduction to Optimization ISBN: 1107053447 ISBN-13(EAN): 9781107053441 Издательство: Cambridge Academ Рейтинг: Цена: 70740.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Assuming only basic linear algebra, this textbook is the perfect starting point for a wide range of undergraduate students from across the mathematical sciences (computer science, engineering, economics, and so on). The authors focus on the fundamental ideas in optimization and motivate the theory with real-world examples and exercises.
Автор: Giraud Название: Introduction to High-Dimensional Statistics ISBN: 1482237946 ISBN-13(EAN): 9781482237948 Издательство: Taylor&Francis Рейтинг: Цена: 64300.00 T Наличие на складе: Нет в наличии. Описание: Ever-greater computing technologies have given rise to an exponentially growing volume of data. Today massive data sets (with potentially thousands of variables) play an important role in almost every branch of modern human activity, including networks, finance, and genetics. However, analyzing such data has presented a challenge for statisticians and data analysts and has required the development of new statistical methods capable of separating the signal from the noise. Introduction to High-Dimensional Statistics is a concise guide to state-of-the-art models, techniques, and approaches for handling high-dimensional data. The book is intended to expose the reader to the key concepts and ideas in the most simple settings possible while avoiding unnecessary technicalities. Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this highly accessible text: Describes the challenges related to the analysis of high-dimensional data Covers cutting-edge statistical methods including model selection, sparsity and the lasso, aggregation, and learning theory Provides detailed exercises at the end of every chapter with collaborative solutions on a wikisite Illustrates concepts with simple but clear practical examples Introduction to High-Dimensional Statistics is suitable for graduate students and researchers interested in discovering modern statistics for massive data. It can be used as a graduate text or for self-study.
Автор: Cumming, Geoff. Название: Introduction to the New Statistics ISBN: 1138825522 ISBN-13(EAN): 9781138825529 Издательство: Taylor&Francis Рейтинг: Цена: 66340.00 T Наличие на складе: Нет в наличии. Описание: This is the first introductory statistics text to use an estimation approach from the start to help readers understand effect sizes, confidence intervals (CIs), and meta-analysis (‘the new statistics’). It is also the first text to explain the new and exciting Open Science practices, which encourage replication and enhance the trustworthiness of research. In addition, the book explains NHST fully so students can understand published research. Numerous real research examples are used throughout. The book uses today’s most effective learning strategies and promotes critical thinking, comprehension, and retention, to deepen users’ understanding of statistics and modern research methods. The free ESCI (Exploratory Software for Confidence Intervals) software makes concepts visually vivid, and provides calculation and graphing facilities. The book can be used with or without ESCI. Other highlights include: - Coverage of both estimation and NHST approaches, and how to easily translate between the two. - Some exercises use ESCI to analyze data and create graphs including CIs, for best understanding of estimation methods. -Videos of the authors describing key concepts and demonstrating use of ESCI provide an engaging learning tool for traditional or flipped classrooms. -In-chapter exercises and quizzes with related commentary allow students to learn by doing, and to monitor their progress. -End-of-chapter exercises and commentary, many using real data, give practice for using the new statistics to analyze data, as well as for applying research judgment in realistic contexts. -Don’t fool yourself tips help students avoid common errors. -Red Flags highlight the meaning of "significance" and what p values actually mean. -Chapter outlines, defined key terms, sidebars of key points, and summarized take-home messages provide a study tool at exam time. -http://www.routledge.com/cw/cumming offers for students: ESCI downloads; data sets; key term flashcards; tips for using SPSS for analyzing data; and videos. For instructors it offers: tips for teaching the new statistics and Open Science; additional homework exercises; assessment items; answer keys for homework and assessment items; and downloadable text images; and PowerPoint lecture slides. Intended for introduction to statistics, data analysis, or quantitative methods courses in psychology, education, and other social and health sciences, researchers interested in understanding the new statistics will also appreciate this book. No familiarity with introductory statistics is assumed.
Автор: Rene L. Schilling, Lothar Partzsch Название: Brownian Motion: An Introduction to Stochastic Processes ISBN: 3110307294 ISBN-13(EAN): 9783110307290 Издательство: Walter de Gruyter Цена: 42450.00 T Наличие на складе: Нет в наличии. Описание: Brownian motion is one of the most important stochastic processes in continuous time and with continuous state space. Within the realm of stochastic processes, Brownian motion is at the intersection of Gaussian processes, martingales, Markov processes, diffusions and random fractals, and it has influenced the study of these topics. Its central position within mathematics is matched by numerous applications in science, engineering and mathematical finance. Often textbooks on probability theory cover, if at all, Brownian motion only briefly. On the other hand, there is a considerable gap to more specialized texts on Brownian motion which is not so easy to overcome for the novice. The authors’ aim was to write a book which can be used as an introduction to Brownian motion and stochastic calculus, and as a first course in continuous-time and continuous-state Markov processes. They also wanted to have a text which would be both a readily accessible mathematical back-up for contemporary applications (such as mathematical finance) and a foundation to get easy access to advanced monographs. This textbook, tailored to the needs of graduate and advanced undergraduate students, covers Brownian motion, starting from its elementary properties, certain distributional aspects, path properties, and leading to stochastic calculus based on Brownian motion. It also includes numerical recipes for the simulation of Brownian motion.
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