Описание: This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.
Автор: Morgan Название: Counterfactuals and Causal Inference ISBN: 1107694167 ISBN-13(EAN): 9781107694163 Издательство: Cambridge Academ Рейтинг: Цена: 5702.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Cause-and-effect questions are the motivation for most research in the social, demographic, and health sciences. The counterfactual approach to causal analysis represents a unified framework for the prosecution of these questions. This second edition aims to convince more social scientists to take this approach when analyzing these core empirical questions.
Автор: Moulin Pierre Название: Statistical Inference for Engineers and Data Scientists ISBN: 1107185920 ISBN-13(EAN): 9781107185920 Издательство: Cambridge Academ Рейтинг: Цена: 10138.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An up-to-date and mathematically accessible introduction to the tools needed to address modern inference problems in engineering and data science. Richly illustrated with examples and exercises connecting the theory with practice, it is the `go to` guide for students studying the topic, and an excellent reference for researchers and practitioners.
Автор: Bradley Efron and Trevor Hastie Название: Computer Age Statistical Inference ISBN: 1107149894 ISBN-13(EAN): 9781107149892 Издательство: Cambridge Academ Рейтинг: Цена: 9029.00 р. 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.
Описание: Cause-and-effect questions are the motivation for most research in the social, demographic, and health sciences. The counterfactual approach to causal analysis represents a unified framework for the prosecution of these questions. This second edition aims to convince more social scientists to take this approach when analyzing these core empirical questions.
Автор: Cheng Russell C H Название: Non-Standard Parametric Statistical Inference ISBN: 0198505043 ISBN-13(EAN): 9780198505044 Издательство: Oxford Academ Рейтинг: Цена: 19404.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This research monograph gives a unified view of non-standard estimation problems. It provides an overall mathematical framework, but also draws together and studies in detail a large number of practical problems, previously only treated separately, offering solution methods and numerical procedures for each.
Автор: Clarke, Bertrand S. (university Of Nebraska, Lincoln) Clarke, Jennifer L. (university Of Nebraska, Lincoln) Название: Predictive statistics ISBN: 1107028280 ISBN-13(EAN): 9781107028289 Издательство: Cambridge Academ Рейтинг: Цена: 12514.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Aimed at statisticians and machine learners, this retooling of statistical theory asserts that high-quality prediction should be the guiding principle of modeling and learning from data, then shows how. The fully predictive approach to statistical problems outlined embraces traditional subfields and `black box` settings, with computed examples.
Автор: R.T.Cox Название: The Algebra of Probable Inference, ISBN: 080186982X ISBN-13(EAN): 9780801869822 Издательство: Wiley Рейтинг: Цена: 4435.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Develops and demonstrates that probability theory is the only theory of inductive inference that abides by logical consistency. The author does this through a functional derivation of probability theory as the unique extension of Boolean Algebra - establishing the legitimacy of Laplace`s theory.
Автор: J.G. Kalbfleisch Название: Probability and Statistical Inference ISBN: 146127009X ISBN-13(EAN): 9781461270096 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A carefully written text, suitable as an introductory course for second or third year students. The main scope of the text guides students towards a critical understanding and handling of data sets together with the ensuing testing of hypotheses.
Автор: Anatolyev, S. Название: Methods for estimation and inference in modern econometrics ISBN: 0367382660 ISBN-13(EAN): 9780367382667 Издательство: Taylor&Francis Рейтинг: Цена: 9798.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Methods for Estimation and Inference in Modern Econometrics provides a comprehensive introduction to a wide range of emerging topics, such as generalized empirical likelihood estimation and alternative asymptotics under drifting parameterizations, which have not been discussed in detail outside of highly technical research papers. The book also addresses several problems often arising in the analysis of economic data, including weak identification, model misspecification, and possible nonstationarity. The book's appendix provides a review of some basic concepts and results from linear algebra, probability theory, and statistics that are used throughout the book.
Topics covered include:
Well-established nonparametric and parametric approaches to estimation and conventional (asymptotic and bootstrap) frameworks for statistical inference
Estimation of models based on moment restrictions implied by economic theory, including various method-of-moments estimators for unconditional and conditional moment restriction models, and asymptotic theory for correctly specified and misspecified models
Non-conventional asymptotic tools that lead to improved finite sample inference, such as higher-order asymptotic analysis that allows for more accurate approximations via various asymptotic expansions, and asymptotic approximations based on drifting parameter sequences
Offering a unified approach to studying econometric problems, Methods for Estimation and Inference in Modern Econometrics links most of the existing estimation and inference methods in a general framework to help readers synthesize all aspects of modern econometric theory. Various theoretical exercises and suggested solutions are included to facilitate understanding.
Finance and insurance companies are facing a wide range of parametric statistical problems. Statistical experiments generated by a sample of independent and identically distributed random variables are frequent and well understood, especially those consisting of probability measures of an exponential type. However, the aforementioned applications also offer non-classical experiments implying observation samples of independent but not identically distributed random variables or even dependent random variables.
Three examples of such experiments are treated in this book. First, the Generalized Linear Models are studied. They extend the standard regression model to non-Gaussian distributions. Statistical experiments with Markov chains are considered next. Finally, various statistical experiments generated by fractional Gaussian noise are also described.
In this book, asymptotic properties of several sequences of estimators are detailed. The notion of asymptotical efficiency is discussed for the different statistical experiments considered in order to give the proper sense of estimation risk. Eighty examples and computations with R software are given throughout the text.
Examines a range of statistical inference methods in the context of finance and insurance applications
Presents the LAN (local asymptotic normality) property of likelihoods
Combines the proofs of LAN property for different statistical experiments that appears in financial and insurance mathematics
Provides the proper description of such statistical experiments and invites readers to seek optimal estimators (performed in R) for such statistical experiments
Автор: Ghosal, Subhashis. Название: Fundamentals of Nonparametric Bayesian Inference ISBN: 0521878268 ISBN-13(EAN): 9780521878265 Издательство: Cambridge Academ Рейтинг: Цена: 12989.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Written by top researchers, this self-contained text is the authoritative account of Bayesian nonparametrics, a nearly universal framework for inference in statistics and machine learning, with practical use in all areas of science, including economics and biostatistics. Appendices with prerequisites and numerous exercises support its use for graduate courses.
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