Essentials of statistical inference, Young, G. A. (imperial College Of Science, Technology And Medicine, London) Smith, R. L. (university Of North Carolina, Chapel Hill)
Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman Название: The Elements of Statistical Learning ISBN: 0387848576 ISBN-13(EAN): 9780387848570 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.
Автор: Bradley Efron , Trevor Hastie Название: Computer Age Statistical Inference, Student Edition ISBN: 1108823416 ISBN-13(EAN): 9781108823418 Издательство: Cambridge Academ Рейтинг: Цена: 5069.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Computing power has revolutionized the theory and practice of statistical inference. Now in paperback, and fortified with 130 class-tested exercises, this book explains modern statistical thinking from classical theories to state-of-the-art prediction algorithms. Anyone who applies statistical methods to data will value this landmark text.
A hands-on approach to statistical inference that addresses the latest developments in this ever-growing field
This clear and accessible book for beginning graduate students offers a practical and detailed approach to the field of statistical inference, providing complete derivations of results, discussions, and MATLAB programs for computation. It emphasizes details of the relevance of the material, intuition, and discussions with a view towards very modern statistical inference. In addition to classic subjects associated with mathematical statistics, topics include an intuitive presentation of the (single and double) bootstrap for confidence interval calculations, shrinkage estimation, tail (maximal moment) estimation, and a variety of methods of point estimation besides maximum likelihood, including use of characteristic functions, and indirect inference. Practical examples of all methods are given. Estimation issues associated with the discrete mixtures of normal distribution, and their solutions, are developed in detail. Much emphasis throughout is on non-Gaussian distributions, including details on working with the stable Paretian distribution and fast calculation of the noncentral Student's t. An entire chapter is dedicated to optimization, including development of Hessian-based methods, as well as heuristic/genetic algorithms that do not require continuity, with MATLAB codes provided.
The book includes both theory and nontechnical discussions, along with a substantial reference to the literature, with an emphasis on alternative, more modern approaches. The recent literature on the misuse of hypothesis testing and p-values for model selection is discussed, and emphasis is given to alternative model selection methods, though hypothesis testing of distributional assumptions is covered in detail, notably for the normal distribution.
Presented in three parts--Essential Concepts in Statistics; Further Fundamental Concepts in Statistics; and Additional Topics--Fundamental Statistical Inference: A Computational Approach offers comprehensive chapters on: Introducing Point and Interval Estimation; Goodness of Fit and Hypothesis Testing; Likelihood; Numerical Optimization; Methods of Point Estimation; Q-Q Plots and Distribution Testing; Unbiased Point Estimation and Bias Reduction; Analytic Interval Estimation; Inference in a Heavy-Tailed Context; The Method of Indirect Inference; and, as an appendix, A Review of Fundamental Concepts in Probability Theory, the latter to keep the book self-contained, and giving material on some advanced subjects such as saddlepoint approximations, expected shortfall in finance, calculation with the stable Paretian distribution, and convergence theorems and proofs.
Автор: Nick T. Thomopoulos Название: Essentials of Monte Carlo Simulation ISBN: 1461460212 ISBN-13(EAN): 9781461460213 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book focuses on the fundamentals of Monte Carlo methods using basic computer simulation techniques. It illustrates the best ways to select input distributions and parameters with or without sample data.
Автор: Proschan, Michael A. , Shaw, Pamela A. Название: Essentials of Probability Theory for Statisticians ISBN: 0367871637 ISBN-13(EAN): 9780367871635 Издательство: Taylor&Francis Рейтинг: Цена: 9645.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This text provides graduate students with a rigorous treatment of probability theory, with an emphasis on results central to theoretical statistics. It presents classical probability theory motivated with illustrative examples in biostatistics, such as outlier tests, monitoring clinical trials, and using adaptive methods to make design changes b
Автор: Proschan, Michael A. Название: Essentials of Probability Theory for Statisticians ISBN: 1498704190 ISBN-13(EAN): 9781498704199 Издательство: Taylor&Francis Рейтинг: Цена: 14086.00 р. Наличие на складе: Поставка под заказ.
Описание: Doubt over the trustworthiness of published empirical results is often a result of statistical mis-specification or invalid probabilistic assumptions. This course in empirical research methods enables the specification and validation of statistical models, facilitating their informed implementation and giving rise to trustworthy evidence.
Автор: Hirschauer, Norbert Gruner, Sven Musshoff, Oliver Название: Fundamentals of Statistical Inference ISBN: 3030990907 ISBN-13(EAN): 9783030990909 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a coherent description of foundational matters concerning statistical inference and shows how statistics can help us make inductive inferences about a broader context, based only on a limited dataset such as a random sample drawn from a larger population.
Описание: This book compiles theoretical developments on statistical inference for time series and related models in honor of Masanobu Taniguchi's 70th birthday. It covers models such as long-range dependence models, nonlinear conditionally heteroscedastic time series, locally stationary processes, integer-valued time series, L?vy Processes, complex-valued time series, categorical time series, exclusive topic models, and copula models. Many cutting-edge methods such as empirical likelihood methods, quantile regression, portmanteau tests, rank-based inference, change-point detection, testing for the goodness-of-fit, higher-order asymptotic expansion, minimum contrast estimation, optimal transportation, and topological methods are proposed, considered, or applied to complex data based on the statistical inference for stochastic processes. The performances of these methods are illustrated by a variety of data analyses. This collection of original papers provides the reader with comprehensive and state-of-the-art theoretical works on time series and related models. It contains deep and profound treatments of the asymptotic theory of statistical inference. In addition, many specialized methodologies based on the asymptotic theory are presented in a simple way for a wide variety of statistical models. This Festschrift finds its core audiences in statistics, signal processing, and econometrics.
Автор: Karr, Alan Название: Point Processes and Their Statistical Inference ISBN: 0367580039 ISBN-13(EAN): 9780367580032 Издательство: Taylor&Francis Рейтинг: Цена: 7501.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Maintaining the excellent features that made the first edition so popular, this outstanding reference/text presents the only comprehensive treatment of the theory of point processes and statistical inference for point processes.
Описание: Mythanksareduetothemanypeoplewhohaveassistedintheworkreported here and in the preparation of this book. The work is incomplete and this account of it rougher than it might be. Such virtues as it has owe much to others; the faults are all mine. MyworkleadingtothisbookbeganwhenDavidBoultonandIattempted to develop a method for intrinsic classi?cation. Given data on a sample from some population, we aimed to discover whether the population should be considered to be a mixture of di?erent types, classes or species of thing, and, if so, how many classes were present, what each class looked like, and which things in the sample belonged to which class. I saw the problem as one of Bayesian inference, but with prior probability densities replaced by discrete probabilities re?ecting the precision to which the data would allow parameters to be estimated. Boulton, however, proposed that a classi?cation of the sample was a way of brie?y encoding the data: once each class was described and each thing assigned to a class, the data for a thing would be partially implied by the characteristics of its class, and hence require little further description. After some weeks arguing our cases, we decided on the maths for each approach, and soon discovered they gave essentially the same results. Without Boulton s insight, we may never have made the connection between inference and brief encoding, which is the heart of this work."
Автор: Roussas George G Название: Introduction to Probability and Statistical Inference ISBN: 0128001143 ISBN-13(EAN): 9780128001141 Издательство: Elsevier Science Рейтинг: Цена: 16505.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
An Introduction to Probability and Statistical Inference, Second Edition, guides you through probability models and statistical methods and helps you to think critically about various concepts. Written by award-winning author George Roussas, this book introduces readers with no prior knowledge in probability or statistics to a thinking process to help them obtain the best solution to a posed question or situation. It provides a plethora of examples for each topic discussed, giving the reader more experience in applying statistical methods to different situations.
This text contains an enhanced number of exercises and graphical illustrations where appropriate to motivate the reader and demonstrate the applicability of probability and statistical inference in a great variety of human activities. Reorganized material is included in the statistical portion of the book to ensure continuity and enhance understanding. Each section includes relevant proofs where appropriate, followed by exercises with useful clues to their solutions. Furthermore, there are brief answers to even-numbered exercises at the back of the book and detailed solutions to all exercises are available to instructors in an Answers Manual.
This text will appeal to advanced undergraduate and graduate students, as well as researchers and practitioners in engineering, business, social sciences or agriculture.
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