Computational Bayesian Statistics: An Introduction, M. Antonia Amaral Turkman, Carlos Daniel Paulino, Peter Muller
Автор: Dougherty Christopher Название: Introduction to Econometrics, 5 ed. ISBN: 0199676828 ISBN-13(EAN): 9780199676828 Издательство: Oxford Academ Рейтинг: Цена: 80250.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Combining the rigour of econometric theory with an accessible style, Dougherty`s step by step explanations and relevant practical exercises ensure students develop an intuitive understanding of econometrics, and gain hands-on experience of the tools used in economic and financial forecasting.
Название: Barron`s AP Statistics, 9th Edition ISBN: 1438009046 ISBN-13(EAN): 9781438009049 Издательство: Kaplan & Barrons Рейтинг: Цена: 16540.00 T Наличие на складе: Невозможна поставка. Описание: This manual's in-depth preparation for the AP Statistics exam features the 35 absolutely best AP Statistics exam hints found anywhere, and includes:
A diagnostic test and five full-length and up-to-date practice exams
All test questions answered and explained
Additional multiple-choice and free-response questions with answers
A 14-chapter subject review, covering all test topics
A new review chapter highlighting statistical insights into social issues
a new chapter on the Investigative Task, which counts as one-eighth of the exam
A guide to basic uses of TI, Casio, and HP graphing calculators
BONUS ONLINE PRACTICE TEST: Students who purchase this book will also get FREE access to one additional full-length online AP Statistics test with all questions answered and explained. Want to boost your studies with even more practice and in-depth review? Try Barron's Ultimate AP Statistics for even more prep.
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.
Автор: Feng Название: An Introduction To Computational Ri ISBN: 1498742165 ISBN-13(EAN): 9781498742160 Издательство: Taylor&Francis Рейтинг: Цена: 117390.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The quantitative modeling of complex systems of interacting risks is a fairly recent development in the financial and insurance industries. Over the past decades, there has been tremendous innovation and development in the actuarial field. In addition to undertaking mortality and longevity risks in traditional life and annuity products, insurers face unprecedented financial risks since the introduction of equity-linking insurance in 1960s. As the industry moves into the new territory of managing many intertwined financial and insurance risks, non-traditional problems and challenges arise, presenting great opportunities for technology development. Today's computational power and technology make it possible for the life insurance industry to develop highly sophisticated models, which were impossible just a decade ago. Nonetheless, as more industrial practices and regulations move towards dependence on stochastic models, the demand for computational power continues to grow. While the industry continues to rely heavily on hardware innovations, trying to make brute force methods faster and more palatable, we are approaching a crossroads about how to proceed. An Introduction to Computational Risk Management of Equity-Linked Insurance provides a resource for students and entry-level professionals to understand the fundamentals of industrial modeling practice, but also to give a glimpse of software methodologies for modeling and computational efficiency. Features Provides a comprehensive and self-contained introduction to quantitative risk management of equity-linked insurance with exercises and programming samples Includes a collection of mathematical formulations of risk management problems presenting opportunities and challenges to applied mathematicians Summarizes state-of-arts computational techniques for risk management professionals Bridges the gap between the latest developments in finance and actuarial literature and the practice of risk management for investment-combined life insurance Gives a comprehensive review of both Monte Carlo simulation methods and non-simulation numerical methods Runhuan Feng is an Associate Professor of Mathematics and the Director of Actuarial Science at the University of Illinois at Urbana-Champaign. He is a Fellow of the Society of Actuaries and a Chartered Enterprise Risk Analyst. He is a Helen Corley Petit Professorial Scholar and the State Farm Companies Foundation Scholar in Actuarial Science. Runhuan received a Ph.D. degree in Actuarial Science from the University of Waterloo, Canada. Prior to joining Illinois, he held a tenure-track position at the University of Wisconsin-Milwaukee, where he was named a Research Fellow. Runhuan received numerous grants and research contracts from the Actuarial Foundation and the Society of Actuaries in the past. He has published a series of papers on top-tier actuarial and applied probability journals on stochastic analytic approaches in risk theory and quantitative risk management of equity-linked insurance. Over the recent years, he has dedicated his efforts to developing computational methods for managing market innovations in areas of investment combined insurance and retirement planning.
Автор: Bolstad Название: Understanding Computational Bayesian Statistics ISBN: 0470046090 ISBN-13(EAN): 9780470046098 Издательство: Wiley Рейтинг: Цена: 135110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach.
Автор: Kokoszka Название: Introduction To Functional Data Ana ISBN: 1498746349 ISBN-13(EAN): 9781498746342 Издательство: Taylor&Francis Рейтинг: Цена: 91860.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The book provides an introduction to functional data analysis (FDA), useful to students and researchers. FDA is now generally viewed as a fundamental subfield of statistics. FDA methods have been applied to science, business and engineering.
Автор: Karl-Rudolf Koch Название: Introduction to Bayesian Statistics ISBN: 3642091830 ISBN-13(EAN): 9783642091834 Издательство: Springer Рейтинг: Цена: 113180.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents Bayes` theorem, the estimation of unknown parameters, the determination of confidence regions and the derivation of tests of hypotheses for the unknown parameters.
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: Scott M. Lynch Название: Introduction to Applied Bayesian Statistics and Estimation for Social Scientists ISBN: 1441924345 ISBN-13(EAN): 9781441924346 Издательство: Springer Рейтинг: Цена: 144410.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.
Автор: Martinez Wendy L. Название: Computational Statistics Handbook with MATLAB ISBN: 1466592737 ISBN-13(EAN): 9781466592735 Издательство: Taylor&Francis Рейтинг: Цена: 107190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
A Strong Practical Focus on Applications and Algorithms Computational Statistics Handbook with MATLAB(R), 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.
Автор: Chave, Alan Dana. Название: Computational statistics in the earth sciences : ISBN: 1107096006 ISBN-13(EAN): 9781107096004 Издательство: Cambridge Academ Рейтинг: Цена: 77090.00 T Наличие на складе: Поставка под заказ. Описание: Based on a course taught by the author, this book combines theoretical underpinnings of statistics with practical analysis of Earth sciences data using MATLAB. Datasets and bespoke MATLAB scripts are available online, as well as questions for use by instructors. This is an ideal text for advanced undergraduate and graduate students.
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: Поставка под заказ. 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.
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