An Introduction to Time Series Analysis and Forecasting, Yaffee, Robert Alan

Автор: Yuniya Kawamura
Название: Doing Research in Fashion and Dress: An Introduction to Qualitative Methods
ISBN: 1350089761 ISBN-13(EAN): 9781350089761
Издательство: Bloomsbury Academic
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Цена: 55440.00 T
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Описание: Whether you’re investigating fashion as a material object, an abstract idea, a social phenomenon, or a commercial system, qualitative techniques can further your understanding of almost any research topic. Doing Research in Fashion and Dress begins by guiding you through a brief history of fashion studies, and the debates surrounding it, before introducing key qualitative methodological approaches, including ethnography, semiology, and object-based research. Detailed case studies demonstrate how each methodology is used in practice. These case studies include Japanese subcultures, fashion photography blogs and semiotic studies of fashion magazine shoots and advertisements. This second edition also features a new chapter on internet sources and online ethnography, reflecting the adoption of social media tools not only by industry practitioners but also by academics.By contextualizing history, theory and practice Doing Research in Fashion and Dress offers:-A systematic examination of qualitative research methods in fashion studies in social sciences. -A practical guide for anyone wishing to conduct fashion research in academia or in the business world.-An accessible grounding in contemporary fashion studies literature.

Автор: James, Gareth Witten, Daniela Hastie, Trevor Tibsh
Название: Introduction to statistical learning
ISBN: 1071614177 ISBN-13(EAN): 9781071614174
Издательство: Springer
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Цена: 55890.00 T
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Описание: 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, deep learning, survival analysis, multiple testing, 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.

This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

Автор: Hamilton, James
Название: Time Series Analysis
ISBN: 0691042896 ISBN-13(EAN): 9780691042893
Издательство: Wiley
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Цена: 73920.00 T
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Описание: A graduate-level text which describes the recent dramatic changes that have taken place in the way that researchers analyze economic and financial time series. It explores such important innovations as vector regression, nonlinear time series models and the generalized methods of moments.

Автор: Giuseppe Da Prato
Название: An Introduction to Infinite-Dimensional Analysis
ISBN: 3642421687 ISBN-13(EAN): 9783642421686
Издательство: Springer
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Цена: 46570.00 T
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Описание: Based on well-known lectures given at Scuola Normale Superiore in Pisa, this book introduces analysis in a separable Hilbert space of infinite dimension. It starts from the definition of Gaussian measures in Hilbert spaces, concepts such as the Cameron-Martin formula, Brownian motion and Wiener integral are introduced in a simple way.

Автор: Llaudet, Elena,
Название: Data analysis for social science :
ISBN: 0691199426 ISBN-13(EAN): 9780691199429
Издательство: Wiley
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Цена: 110880.00 T
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Описание: An ideal textbook for an introductory course on quantitative methods for social scientists-assumes no prior knowledge of statistics or codingData Analysis for Social Science provides a friendly introduction to the statistical concepts and programming skills needed to conduct and evaluate social scientific studies. Using plain language and assuming no prior knowledge of statistics and coding, the book provides a step-by-step guide to analyzing real-world data with the statistical program R for the purpose of answering a wide range of substantive social science questions. It teaches not only how to perform the analyses but also how to interpret results and identify strengths and limitations.

This one-of-a-kind textbook includes supplemental materials to accommodate students with minimal knowledge of math and clearly identifies sections with more advanced material so that readers can skip them if they so choose. Analyzes real-world data using the powerful, open-sourced statistical program R, which is free for everyone to useTeaches how to measure, predict, and explain quantities of interest based on dataShows how to infer population characteristics using survey research, predict outcomes using linear models, and estimate causal effects with and without randomized experimentsAssumes no prior knowledge of statistics or codingSpecifically designed to accommodate students with a variety of math backgroundsProvides cheatsheets of statistical concepts and R codeSupporting materials available online, including real-world datasets and the code to analyze them, plus-for instructor use-sample syllabi, sample lecture slides, additional datasets, and additional exercises with solutionsLooking for a more advanced introduction? Consider Quantitative Social Science by Kosuke Imai. In addition to covering the material in Data Analysis for Social Science, it teaches diffs-in-diffs models, heterogeneous effects, text analysis, and regression discontinuity designs, among other things.

Автор: Maurizio Petrelli
Название: Introduction to Python in Earth Science Data Analysis
ISBN: 3030780546 ISBN-13(EAN): 9783030780548
Издательство: Springer
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Цена: 50810.00 T
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Описание: This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences.

Автор: Bacci Silvia, Chiandotto Bruno
Название: Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis
ISBN: 1032091754 ISBN-13(EAN): 9781032091754
Издательство: Taylor&Francis
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Цена: 50010.00 T
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Описание: This book provides the theoretical background to approach decision theory from a statistical perspective. It covers both traditional approaches, in terms of value theory and expected utility theory, and recent developments, in terms of causal inference.

Автор: Box G. E. P.
Название: Time Series Analysis: Forecasting and Control, 4th Edition
ISBN: 0470272848 ISBN-13(EAN): 9780470272848
Издательство: Wiley
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Цена: 123550.00 T
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Описание: This is a revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970. It focuses on practical techniques throughout, rather than a rigorous mathematical treatment of the subject. It explores the building of stochastic (statistical) models for time series and their use in important areas of application forecasting, model specification, estimation, modeling the effects of intervention events, and process control, among others. In addition to meticulous modifications in content and improvements in style, the new edition incorporates several new topics in an effort to modernize the subject matter. These topics include extensive discussions of multivariate time series, smoothing, likelihood function based on the state space model, autoregressive models, structural component models and deterministic seasonal components, and nonlinear and long memory models.

Автор: Box George E. P.
Название: Time Series Analysis
ISBN: 1118675029 ISBN-13(EAN): 9781118675021
Издательство: Wiley
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Цена: 134060.00 T
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Описание: Praise for the Fourth Edition The book follows faithfully the style of the original edition. The approach is heavily motivated by real-world time series, and by developing a complete approach to model building, estimation, forecasting and control.

Автор: Jan G. De Gooijer
Название: Elements of Nonlinear Time Series Analysis and Forecasting
ISBN: 3319432516 ISBN-13(EAN): 9783319432519
Издательство: Springer
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Цена: 121110.00 T
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Описание: This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications.

Автор: Chatfield
Название: The Analysis Of Time Series 7E
ISBN: 1498795633 ISBN-13(EAN): 9781498795630
Издательство: Taylor&Francis
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Цена: 78590.00 T
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Описание: This new edition of this classic title, now in its seventh edition, presents a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis.

Автор: T.S. Rao; M.M. Gabr
Название: An Introduction to Bispectral Analysis and Bilinear Time Series Models
ISBN: 0387960392 ISBN-13(EAN): 9780387960395
Издательство: Springer
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Цена: 111790.00 T
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Описание: The theory of time series models has been well developed over the last thirt,y years. The most interesting feature of such a model is that its second order covariance analysis is ve~ similar to that for a linear model. This demonstrates the importance of higher order covariance analysis for nonlinear models.