Описание: 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.
Описание: This major new textbook from a distinguished econometrician is intended for students taking introductory courses in probability theory and statistical inference. No prior knowledge other than a basic familiarity with descriptive statistics is assumed. The primary objective of this book is to establish the framework for the empirical modelling of observational (non-experimental) data. This framework known as 'Probabilistic Reduction' is formulated with a view to accommodating the peculiarities of observational (as opposed to experimental) data in a unifying and logically coherent way. Probability Theory and Statistical Inference differs from traditional textbooks in so far as it emphasizes concepts, ideas, notions and procedures which are appropriate for modelling observational data. Aimed at students at second-year undergraduate level and above studying econometrics and economics, this textbook will also be useful for students in other disciplines which make extensive use of observational data, including finance, biology, sociology and psychology and climatology.
Описание: The primary aims of this book are to provide modern statistical techniques and theory for stochastic processes. The stochastic processes mentioned here are
not restricted to the usual AR, MA and ARMA processes. A wide variety of stochastic processes, e.g., non-Gaussian linear processes, long-memory processes, nonlinear processes,
non-ergodic processes and diffusion processes are described.
The authors discuss the usual estimation and testing theory and also many other statistical methods and
techniques, e.g., discriminant analysis, nonparametric methods, semiparametric approaches, higher order asymptotic theory in view of differential geometry, large deviation principle and
saddlepoint approximation. Because it is difficult to use the exact distribution theory, the discussion is based on the asymptotic theory. The optimality of various procedures is often
shown by use of the local asymptotic normality (LAN) which is due to Le Cam.
The LAN gives a unified view for th
time series asymptotic theory.
Описание: 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.
Описание: Quantum statistical inference, a research field with deep roots in the foundations of both quantum physics and mathematical statistics, has made remarkable
progress since 1990. In particular, its asymptotic theory has been developed during this period. However, there has hitherto been no book covering this remarkable progress after 1990;
the famous textbooks by Holevo and Helstrom deal only with research results in the earlier stage (1960s-1970s).
This book presents the important and recent results of
quantum statistical inference. It focuses on the asymptotic theory, which is one of the central issues of mathematical statistics and had not been investigated in quantum statistical
inference until the early 1980s. It contains outstanding papers after Holevo's textbook, some of which are of great importance but are not available now.
The reader is
expected to have only elementary mathematical knowledge, and therefore much of the content will be accessible to graduate students as well as research workers in related fields.
Introductions to quantum statistical inference have been specially written for the book. Asymptotic Theory of Quantum Statistical Inference: Selected Papers will give the reader a new
insight into physics and statistical inference.
Автор: G. A. Young Название: Essentials of Statistical Inference ISBN: 0521839718 ISBN-13(EAN): 9780521839716 Издательство: Cambridge Academ Рейтинг: Цена: 7597 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Описание: Because the field of financial engineering integrates multiple disciplines, it is important that stochastic models describe financial assets sufficiently. This book presents an introduction to the optimal inference of financial engineering models and demonstrates how to properly estimate the proposed models.
Описание: Discusses the estimation theory for the wide class of inhomogeneous Poisson processes. This book investigates the maximum likelihood, Bayesian, and the minimum distance estimators in parametric problems and studies the empiric intensity measure and the kernel-type estimators in nonparametric estimation problems.
Автор: D. R. Cox Название: Principles of Statistical Inference ISBN: 0521685672 ISBN-13(EAN): 9780521685672 Издательство: Cambridge Academ Рейтинг: Цена: 3642 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Описание: With amusing anecdotes and trivia, this text explains how statistical methods are used for data analysis and uses the elementary functions of R to perform the individual steps of statistical procedures. It introduces basic concepts of inference through a careful study of several important procedures, including parametric and nonparametric methods, analysis of variance, and regression. The text also presents many applications, supporting data sets, and end-of-chapter exercises. The R code and data sets are available for download online and a solutions manual is available for qualifying instructors.
Автор: Cox Название: Principles of Statistical Inference ISBN: 0521866731 ISBN-13(EAN): 9780521866736 Издательство: Cambridge Academ Рейтинг: Цена: 7494 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The comprehensive, balanced account of the theory of statistical inference, its main ideas and controversies.
Описание: Nonparametric techniques in statistics are those in which the data are ranked in order according to some particular characteristic. When applied to measurable characteristics, the use of such techniques often saves considerable calculation as compared with more formal methods, with only slight loss of accuracy. The field of nonparametric statistics is occupying an increasingly important role in statistical theory as well as in its applications. Nonparametric methods are mathematically elegant, and they also yield significantly improved performances in applications to agriculture, education, biometrics, medicine, communication, economics and industry.
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