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Statistical Inference in Finan cial and Insurance Mathematics with R, Brouste Alexandre



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Автор: Brouste Alexandre
Название:  Statistical Inference in Finan cial and Insurance Mathematics with R   (Александр Бруст: Статистические выводы в финансах и страховании)
Издательство: Elsevier Science
Классификация:
Финансы и бухгалтерский учет
Бухгалтерский учет
Финансы
Вероятность и статистика

ISBN: 1785480839
ISBN-13(EAN): 9781785480836
ISBN: 1-78548-083-9
ISBN-13(EAN): 978-1-78548-083-6
Обложка/Формат: Hardcover
Страницы: 150
Вес: 0.521 кг.
Дата издания: 22.11.2017
Серия: Psych Neuro&For
Язык: ENG
Размер: 236 x 158 x 18
Читательская аудитория: Professional & vocational
Ссылка на Издательство: Link
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Поставляется из: Англии
Описание: 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.
Дополнительное описание:




Causal Inference for Statistics, Social, and Biomedical Sciences

Автор: Imbens
Название: Causal Inference for Statistics, Social, and Biomedical Sciences
ISBN: 0521885884 ISBN-13(EAN): 9780521885881
Издательство: Cambridge Academ
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Цена: 4683 р.
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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.

Probability Theory and Statistical Inference

Название: Probability Theory and Statistical Inference
ISBN: 0521424089 ISBN-13(EAN): 9780521424080
Издательство: Cambridge Academ
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Цена: 4787 р.
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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.

Asymptotic Theory of Statistical Inference for Time Series

Автор: Taniguchi
Название: Asymptotic Theory of Statistical Inference for Time Series
ISBN: 0387950397 ISBN-13(EAN): 9780387950396
Издательство: Springer
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Цена: 18232 р.
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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.

Advances in statistical modeling and inference: essays in honor of kjell a doksum

Название: Advances in statistical modeling and inference: essays in honor of kjell a doksum
ISBN: 9812703691 ISBN-13(EAN): 9789812703699
Издательство: World Scientific Publishing
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Цена: 30357 р.
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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.

Asymptotic theory of quantum statistical inference: selected papers

Название: Asymptotic theory of quantum statistical inference: selected papers
ISBN: 9812560157 ISBN-13(EAN): 9789812560155
Издательство: World Scientific Publishing
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Цена: 19671 р.
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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.

Essentials of Statistical Inference

Автор: G. A. Young
Название: Essentials of Statistical Inference
ISBN: 0521839718 ISBN-13(EAN): 9780521839716
Издательство: Cambridge Academ
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Цена: 7597 р.
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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.

Optimal statistical inference in financial engineering

Автор: Taniguchi, Masanobu Hirukawa, Junichi Tamaki, Keni
Название: Optimal statistical inference in financial engineering
ISBN: 1584885912 ISBN-13(EAN): 9781584885917
Издательство: Taylor&Francis
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Цена: 10450 р.
Наличие на складе: Невозможна поставка.

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.

Statistical Inference for Spatial Poisson Processes

Автор: Kutoyants
Название: Statistical Inference for Spatial Poisson Processes
ISBN: 038798562X ISBN-13(EAN): 9780387985626
Издательство: Springer
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Цена: 13089 р.
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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.

Principles of Statistical Inference

Автор: D. R. Cox
Название: Principles of Statistical Inference
ISBN: 0521685672 ISBN-13(EAN): 9780521685672
Издательство: Cambridge Academ
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Цена: 3642 р.
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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.

An Introduction to Statistical Inference and Its Applications with R

Автор: Trosset
Название: An Introduction to Statistical Inference and Its Applications with R
ISBN: 1584889470 ISBN-13(EAN): 9781584889472
Издательство: Taylor&Francis
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Цена: 7418 р.
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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.

Principles of Statistical Inference

Автор: Cox
Название: Principles of Statistical Inference
ISBN: 0521866731 ISBN-13(EAN): 9780521866736
Издательство: Cambridge Academ
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Цена: 7494 р.
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Описание: The comprehensive, balanced account of the theory of statistical inference, its main ideas and controversies.

Nonparametric Techniques in Statistical Inference

Автор: Puri
Название: Nonparametric Techniques in Statistical Inference
ISBN: 0521093058 ISBN-13(EAN): 9780521093057
Издательство: Cambridge Academ
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Цена: 4579 р.
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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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