Statistical Dynamics and Reliability Theory for Mechanical Structures, Nikolay Reshetov; Valery A. Svetlitsky

Автор: Kohler
Название: Data Analysis Using Stata, Third Edition
ISBN: 1597181102 ISBN-13(EAN): 9781597181105
Издательство: Taylor&Francis
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Цена: 74510.00 T
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Data Analysis Using Stata, Third Edition is a comprehensive introduction to both statistical methods and Stata. Beginners will learn the logic of data analysis and interpretation and easily become self-sufficient data analysts. Readers already familiar with Stata will find it an enjoyable resource for picking up new tips and tricks.

The book is written as a self-study tutorial and organized around examples. It interactively introduces statistical techniques such as data exploration, description, and regression techniques for continuous and binary dependent variables. Step by step, readers move through the entire process of data analysis and in doing so learn the principles of Stata, data manipulation, graphical representation, and programs to automate repetitive tasks. This third edition includes advanced topics, such as factor-variables notation, average marginal effects, standard errors in complex survey, and multiple imputation in a way, that beginners of both data analysis and Stata can understand.

Using data from a longitudinal study of private households, the authors provide examples from the social sciences that are relatable to researchers from all disciplines. The examples emphasize good statistical practice and reproducible research. Readers are encouraged to download the companion package of datasets to replicate the examples as they work through the book. Each chapter ends with exercises to consolidate acquired skills.

Автор: Becketti
Название: Introduction to Time Series Using Stata
ISBN: 1597181323 ISBN-13(EAN): 9781597181327
Издательство: Taylor&Francis
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Цена: 75530.00 T
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Описание: Recent decades have witnessed explosive growth in new and powerful tools for timeseries analysis. These innovations have overturned older approaches to forecasting, macroeconomic policy analysis, the study of productivity and long-run economic growth, and the trading of financial assets. Familiarity with these new tools on time series is an essential skill for statisticians, econometricians, and applied researchers. Introduction to Time Series Using Stata provides a step-by-step guide to essential timeseries techniques—from the incredibly simple to the quite complex—and, at the same time, demonstrates how these techniques can be applied in the Stata statistical package. The emphasis is on an understanding of the intuition underlying theoretical innovations and an ability to apply them. Real-world examples illustrate the application of each concept as it is introduced, and care is taken to highlight the pitfalls, as well as the power, of each new tool. Sean Becketti is a financial industry veteran with three decades of experience in academics, government, and private industry. Over the last two decades, Becketti has led proprietary research teams at several leading financial firms, responsible for the models underlying the valuation, hedging, and relative value analysis of some of the largest fixed-income portfolios in the world.

Автор: Marvin Rausand
Название: System Reliability Theory: Models, Statistical Methods, and Applications, 2nd Edition
ISBN: 047147133X ISBN-13(EAN): 9780471471332
Издательство: Wiley
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Цена: 162630.00 T
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Описание: An examination of system reliability theory, this title features in-depth discussion of dependability management and reliability centered systems as well as functional safety issues-matters critical to the IEC standards.

Автор: Wael Bahsoun; Christopher Bose; Gary Froyland
Название: Ergodic Theory, Open Dynamics, and Coherent Structures
ISBN: 1493904183 ISBN-13(EAN): 9781493904181
Издательство: Springer
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Цена: 139750.00 T
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Описание: This book is comprised of selected research articles developed from a workshop on Ergodic Theory, Probabilistic Methods and Applications, held in April 2012 at the Banff International Research Station.

Автор: Wael Bahsoun; Christopher Bose; Gary Froyland
Название: Ergodic Theory, Open Dynamics, and Coherent Structures
ISBN: 149394326X ISBN-13(EAN): 9781493943265
Издательство: Springer
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Цена: 102480.00 T
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Описание: This book is comprised of selected research articles developed from a workshop on Ergodic Theory, Probabilistic Methods and Applications, held in April 2012 at the Banff International Research Station.

Автор: Hastings
Название: Introduction to Probability with Mathematica, Second Edition
ISBN: 1420079387 ISBN-13(EAN): 9781420079388
Издательство: Taylor&Francis
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Цена: 183750.00 T
Наличие на складе: Невозможна поставка.
Описание: Updated to conform to Mathematica (R) 7.0, this second edition shows how to easily create simulations from templates and solve problems using Mathematica. Along with new sections on order statistics, transformations of multivariate normal random variables, and Brownian motion, this edition offers an expanded section on

Автор: Williams, Martin
Название: Structures:Theory and Analysis
ISBN: 0333677609 ISBN-13(EAN): 9780333677605
Издательство: Springer
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Цена: 68930.00 T
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Описание: This textbook aims to encompasses the full range of material covered in undergraduate courses in Structures in departments of Civil and Mechanical Engineering. The approach taken integrates a qualitative approach looking at the physical reality of phenomena with a quantitative approach.

Автор: Nikolaos Limnios; Dumitru Cezar Ionescu
Название: Statistical and Probabilistic Models in Reliability
ISBN: 1461272807 ISBN-13(EAN): 9781461272809
Издательство: Springer
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Цена: 186330.00 T
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Описание: This volume consists of twenty-four papers selected by the editors from the sixty-one papers presented at the 1st International Conference on Mathemati cal Methods in Reliability held at the Politehnica University of Bucharest from 16 to 19 September 1997.

Автор: Gamerman, Dani.
Название: Markov Chain Monte Carlo
ISBN: 1584885874 ISBN-13(EAN): 9781584885870
Издательство: Taylor&Francis
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Цена: 102080.00 T
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Описание: Incorporating changes in theory and highlighting various applications, this book presents a comprehensive introduction to the methods of Markov Chain Monte Carlo (MCMC) simulation technique. It incorporates the developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection.

Автор: Nikolay Reshetov; Valery A. Svetlitsky
Название: Statistical Dynamics and Reliability Theory for Mechanical Structures
ISBN: 3642536573 ISBN-13(EAN): 9783642536571
Издательство: Springer
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Цена: 149060.00 T
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Автор: Hastie
Название: Statistical Learning with Sparsity
ISBN: 1498712169 ISBN-13(EAN): 9781498712163
Издательство: Taylor&Francis
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Цена: 112290.00 T
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Discover New Methods for Dealing with High-Dimensional Data

A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data.

Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of ℓ1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso.

In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.