Àâòîð: Robert Hogg Íàçâàíèå: Probability and Statistical Inference: United States Edition ISBN: 0131464132 ISBN-13(EAN): 9780131464131 Èçäàòåëüñòâî: Pearson Education Öåíà: 3717 ð. Íàëè÷èå íà ñêëàäå: Íåâîçìîæíà ïîñòàâêà. Îïèñàíèå: This applied introduction to the mathematics of probability and statistics emphasizes the existence of variation in almost every process, and how the study of
probability and statistics helps us understand this variability. Designed for students with a background in calculus, it reinforces basic mathematical concepts with numerous real-world
examples and applications to illustrate the relevance of key concepts.

Àâòîð: Hogg Robert Íàçâàíèå: Probability and Statistical Inference ISBN: 032163635X ISBN-13(EAN): 9780321636355 Èçäàòåëüñòâî: Pearson Education Öåíà: 3717 ð. Íàëè÷èå íà ñêëàäå: Íåâîçìîæíà ïîñòàâêà.

Àâòîð: Robert Hogg Elliot Tanis Íàçâàíèå: Probability and Statistical Inference, Global Edition ISBN: 1292062355 ISBN-13(EAN): 9781292062358 Èçäàòåëüñòâî: Pearson Education Öåíà: 4208 ð. Íàëè÷èå íà ñêëàäå: Ïîñòàâêà ïîä çàêàç. Îïèñàíèå: For a one- or two-semester course; calculus background presumed, no previous study of probability or statistics is required. Written by three veteran statisticians, this applied introduction to probability and statistics emphasizes the existence of variation in almost every process, and how the study of probability and statistics helps us understand this variation. Designed for students with a background in calculus, this book continues to reinforce basic mathematical concepts with numerous real-world examples and applications to illustrate the relevance of key concepts.

Îïèñàíèå: Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.

Îïèñàíèå: This gracefully organized text reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises,
figures, tables, and computer simulations to develop and illustrate concepts. Drills and boxed summaries emphasize and reinforce important ideas and special techniques. Beginning with
a review of the basic concepts and methods in probability theory, moments, and moment generating functions, the author moves to more intricate topics.

"Introductory
Statistical Inference" studies multivariate random variables, exponential families of distributions, and standard probability inequalities. It develops the Helmert transformation for normal
distributions, introduces the notions of convergence, and spotlights the central limit theorems.In this text, coverage highlights sampling distributions, Basu's theorem,
Rao-Blackwellization and the Cramer-Rao inequality. The text also provides in-depth coverage of Lehmann-Scheffe theorems, focuses on tests of hypotheses, describes Bayesian
methods and the Bayes' estimator, and develops large-sample inference.

The author provides a historical context for statistics and statistical discoveries and answers to a
majority of the end-of-chapter exercises. Designed primarily for a one-semester, first-year graduate course in probability and statistical inference, this text serves readers from varied
backgrounds, ranging from engineering, economics, agriculture, and bioscience to finance, financial mathematics, operations and information management, and psychology.

Îïèñàíèå: This volume is a compressed survey containing recent results on statistics of stochastic processes and on identification with incomplete observations. It comprises a collection of papers presented at the Shoresh Conference 2000 on the Foundation of Statistical Inference. The papers cover the following areas with high research activity:- Identification with Incomplete Observations, Data Mining,- Bayesian Methods and Modelling,- Testing, Goodness of Fit and Randomness,- Statistics of Stationary Processes.

Îïèñàíèå: The Minimum Message Length (MML) Principle is an information-theoretic approach to induction, hypothesis testing, model selection, and statistical inference. MML, which provides a formal specification for the implementation of Occam's Razor, asserts that the â€˜bestâ€™ explanation of observed data is the shortest. Further, an explanation is acceptable (i.e. the induction is justified) only if the explanation is shorter than the original data.This book gives a sound introduction to the Minimum Message Length Principle and its applications, provides the theoretical arguments for the adoption of the principle, and shows the development of certain approximations that assist its practical application. MML appears also to provide both a normative and a descriptive basis for inductive reasoning generally, and scientific induction in particular. The book describes this basis and aims to show its relevance to the Philosophy of Science.Statistical and Inductive Inference by Minimum Message Length will be of special interest to graduate students and researchers in Machine Learning and Data Mining, scientists and analysts in various disciplines wishing to make use of computer techniques for hypothesis discovery, statisticians and econometricians interested in the underlying theory of their discipline, and persons interested in the Philosophy of Science. The book could also be used in a graduate-level course in Machine Learning and Estimation and Model-selection, Econometrics and Data Mining."Any statistician interested in the foundations of the discipline, or the deeper philosophical issues of inference, will find this volume a rewarding read." Short Book Reviews of the International Statistical Institute, December 2005

Àâòîð: Sunil K. Mathur Íàçâàíèå: Probability and Statistical Inference Using R, ISBN: 012386982X ISBN-13(EAN): 9780123869821 Èçäàòåëüñòâî: Elsevier Science Öåíà: 6634 ð. Íàëè÷èå íà ñêëàäå: Íåâîçìîæíà ïîñòàâêà.

Îïèñàíèå: This volumes focuses on the theory of statistical inference under inequality constraints, providing a unified and up-to-date treatment of the methodology. The scope of applications of the presented methodology and theory in different fields is clearly illustrated by using examples from several areas, especially sociology, econometrics,d biostatistics. The authors also discuss a broad range of other inequality constrained inference problems, which do not fit well in the contemplated unified framework, providing meaningful access to comprehend methodological resolutions.

Îïèñàíèå: This is a history of parametric statistical inference, written by one of the most important historians of statistics of the 20th century, Anders Hald. This book can be viewed as a follow-up to his two most recent books, although this current text is much more streamlined and contains new analysis of many ideas and developments. And unlike his other books, which were encyclopedic by nature, this book can be used for a course on the topic, the only prerequisites being a basic course in probability and statistics.The book is divided into five main sections:* Binomial statistical inference;* Statistical inference by inverse probability;* The central limit theorem and linear minimum variance estimation by Laplace and Gauss;* Error theory, skew distributions, correlation, sampling distributions;* The Fisherian Revolution, 1912-1935.Throughout each of the chapters, the author provides lively biographical sketches of many of the main characters, including Laplace, Gauss, Edgeworth, Fisher, and Karl Pearson. He also examines the roles played by DeMoivre, James Bernoulli, and Lagrange, and he provides an accessible exposition of the work of R.A. Fisher.This book will be of interest to statisticians, mathematicians, undergraduate and graduate students, and historians of science.

Àâòîð: Kutoyants Yury A. Íàçâàíèå: Statistical Inference for Ergodic Diffusion Processes ISBN: 1852337591 ISBN-13(EAN): 9781852337599 Èçäàòåëüñòâî: Springer Ðåéòèíã: Öåíà: 14492 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.

Îïèñàíèå: Statistical Inference for Ergodic Diffusion Processes encompasses a wealth of results from over ten years of mathematical literature. It provides a comprehensive overview of existing techniques, and presents - for the first time in book form - many new techniques and approaches. An elementary introduction to the field at the start of the book introduces a class of examples - both non-standard and classical - that reappear as the investigation progresses to illustrate the merits and demerits of the procedures. The statements of the problems are in the spirit of classical mathematical statistics, and special attention is paid to asymptotically efficient procedures. Today, diffusion processes are widely used in applied problems in fields such as physics, mechanics and, in particular, financial mathematics. This book provides a state-of-the-art reference that will prove invaluable to researchers, and graduate and postgraduate students, in areas such as financial mathematics, economics, physics, mechanics and the biomedical sciences.From the reviews:"This book is very much in the Springer mould of graduate mathematical statistics books, giving rapid access to the latest literature...It presents a strong discussion of nonparametric and semiparametric results, from both classical and Bayesian standpoints...I have no doubt that it will come to be regarded as a classic text." Journal of the Royal Statistical Society, Series A, v. 167

Àâòîð: D. R. Cox Íàçâàíèå: Principles of Statistical Inference ISBN: 0521685672 ISBN-13(EAN): 9780521685672 Èçäàòåëüñòâî: Cambridge Academ Ðåéòèíã: Öåíà: 3642 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.

Îïèñàíèå: In this definitive book, D. R. Cox gives a comprehensive and balanced appraisal of statistical inference. He develops the key concepts, describing and comparing the main ideas and controversies over foundational issues that have been keenly argued for more than two-hundred years. Continuing a sixty-year career of major contributions to statistical thought, no one is better placed to give this much-needed account of the field. An appendix gives a more personal assessment of the merits of different ideas. The content ranges from the traditional to the contemporary. While specific applications are not treated, the book is strongly motivated by applications across the sciences and associated technologies. The mathematics is kept as elementary as feasible, though previous knowledge of statistics is assumed. The book will be valued by every user or student of statistics who is serious about understanding the uncertainty inherent in conclusions from statistical analyses.

Àâòîð: G. A. Young Íàçâàíèå: Essentials of Statistical Inference ISBN: 0521839718 ISBN-13(EAN): 9780521839716 Èçäàòåëüñòâî: Cambridge Academ Ðåéòèíã: Öåíà: 7597 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.

Îïèñàíèå: This engaging textbook presents the concepts and results underlying the Bayesian, frequentist and Fisherian approaches to statistical inference, with particular emphasis on the contrasts between them. Aimed at advanced undergraduates and graduate students in mathematics and related disciplines, it covers in a concise treatment both basic mathematical theory and more advanced material, including such contemporary topics as Bayesian computation, higher-order likelihood theory, predictive inference, bootstrap methods and conditional inference. It contains numerous extended examples of the application of formal inference techniques to real data, as well as historical commentary on the development of the subject. Throughout, the text concentrates on concepts, rather than mathematical detail, while maintaining appropriate levels of formality. Each chapter ends with a set of accessible problems. Some prior knowledge of probability is assumed, while some previous knowledge of the objectives and main approaches to statistical inference would be helpful but is not essential.

Îïèñàíèå: There have been major developments in the field of statistics over the last quarter century, spurred by the rapid advances in computing and data-measurement
technologies. These developments have revolutionized the field and have greatly influenced research directions in theory and methodology. Increased computing power has spawned
entirely new areas of research in computationally-intensive methods, allowing us to move away from narrowly applicable parametric techniques based on restrictive assumptions to much
more flexible and realistic models and methods.

These computational advances have also led to the extensive use of simulation and Monte Carlo techniques in statistical
inference. All of these developments have, in turn, stimulated new research in theoretical statistics. This volume provides an up-to-date overview of recent advances in statistical
modeling and inference.

Written by renowned researchers from across the world, it discusses flexible models, semi-parametric methods and transformation models,
nonparametric regression and mixture models, survival and reliability analysis, and re-sampling techniques. With its coverage of methodology and theory as well as applications, the
book is an essential reference for researchers, graduate students, and practitioners.

Îïèñàíèå: A superb resource on statistical inference for researchers or students, this book has R code throughout, including in sample problems, and an appendix of derived notation and formulae. It covers core topics as well as modern aspects such as M-estimation.