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Confidence, Likelihood, Probability, Schweder

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Цена: 12291р.
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Автор: Schweder
Название:  Confidence, Likelihood, Probability
ISBN: 9780521861601
Издательство: Cambridge Academ
ISBN-10: 0521861608
Обложка/Формат: Hardback
Страницы: 511
Вес: 1.096 кг.
Дата издания: 24.02.2016
Серия: Cambridge series in statistical and probabilistic mathematics
Язык: English
Иллюстрации: Worked examples or exercises; 17 tables, unspecified; 147 line drawings, unspecified
Размер: 189 x 261 x 33
Читательская аудитория: Tertiary education (us: college)
Ключевые слова: Economics,Econometrics,Probability & statistics, MATHEMATICS / Probability & Statistics / General
Основная тема: Statistics and probability
Подзаголовок: Statistical Inference with Confidence Distributions
Ссылка на Издательство: Link
Поставляется из: Англии

Local Regression and Likelihood

Автор: Loader
Название: Local Regression and Likelihood
ISBN: 0387987754 ISBN-13(EAN): 9780387987750
Издательство: Springer
Цена: 24501 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Gives you 2,000 problems in discrete mathematics. This guide helps you to master various types of problems you will face on your tests, from simple questions on set theory to complex Boolean algebra, logic gates, and the use of propositional calculus. Smoothing methods play an important role in many areas of statistics. This book explains how to implement these methods in several popular statistical programs including S-PLUS.

Empirical Likelihood

Автор: Owen
Название: Empirical Likelihood
ISBN: 1584880716 ISBN-13(EAN): 9781584880714
Издательство: Taylor&Francis
Цена: р.
Наличие на складе: Невозможна поставка.

Описание: One of the first books published on the subject, Empirical Likelihood offers an in-depth treatment of this method for constructing confidence regions and testing hypotheses. The author applies the method to a range of problems, from those as simple as setting a confidence region for a univariate mean under IID sampling, to problems defined through smooth functions of means, regression models, generalized linear models, estimating equations, or kernel smooths, and to sampling with non-identically distributed data. Numerous examples from a variety of disciplines and detailed descriptions of algorithms-also posted on a companion Web site-illustrate the methods in practice.

Empirical Likelihood Method in Survival Analysis

Автор: Zhou
Название: Empirical Likelihood Method in Survival Analysis
ISBN: 1466554924 ISBN-13(EAN): 9781466554924
Издательство: Taylor&Francis
Цена: 14518 р.
Наличие на складе: Поставка под заказ.

Описание: Add the Empirical Likelihood to Your Nonparametric Toolbox Empirical Likelihood Method in Survival Analysis explains how to use the empirical likelihood method for right censored survival data. The author uses R for calculating empirical likelihood and includes many worked out examples with the associated R code. The datasets and code are available for download on his website and CRAN. The book focuses on all the standard survival analysis topics treated with empirical likelihood, including hazard functions, cumulative distribution functions, analysis of the Cox model, and computation of empirical likelihood for censored data. It also covers semi-parametric accelerated failure time models, the optimality of confidence regions derived from empirical likelihood or plug-in empirical likelihood ratio tests, and several empirical likelihood confidence band results. While survival analysis is a classic area of statistical study, the empirical likelihood methodology has only recently been developed. Until now, just one book was available on empirical likelihood and most statistical software did not include empirical likelihood procedures. Addressing this shortfall, this book provides the functions to calculate the empirical likelihood ratio in survival analysis as well as functions related to the empirical likelihood analysis of the Cox regression model and other hazard regression models.

In All Likelihood

Автор: Pawitan Yudi
Название: In All Likelihood
ISBN: 0199671222 ISBN-13(EAN): 9780199671229
Издательство: Oxford Academ
Цена: 8223 р.
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Описание: This book introduces likelihood as a unifying concept in statistical modelling and inference. The complete range of concepts and applications are covered, from very simple to very complex studies. It relies on realistic examples, and presents the main results using heuristic rather than formal mathematical arguments.

Maximum likelihood estimation and inference

Автор: Millar, Russell
Название: Maximum likelihood estimation and inference
ISBN: 0470094826 ISBN-13(EAN): 9780470094822
Издательство: Wiley
Цена: 14520 р.
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Описание: Applied Likelihood Methods provides an accessible and practical introduction to likelihood modeling, supported by examples and software. The book features applications from a range of disciplines, including statistics, medicine, biology, and ecology.

Maximum Penalized Likelihood Estimation

Автор: Paul P. Eggermont; Vincent N. LaRiccia
Название: Maximum Penalized Likelihood Estimation
ISBN: 0387402675 ISBN-13(EAN): 9780387402673
Издательство: Springer
Цена: 24501 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This is the second volume of a text on the theory and practice of maximum penalized likelihood estimation. It is intended for graduate students in statistics, operations research and applied mathematics, as well as for researchers and practitioners in the field. The present volume deals with nonparametric regression.

The emphasis in this volume is on smoothing splines of arbitrary order, but other estimators (kernels, local and global polynomials) pass review as well. Smoothing splines and local polynomials are studied in the context of reproducing kernel Hilbert spaces. The connection between smoothing splines and reproducing kernels is of course well-known.

The new twist is that letting the innerproduct depend on the smoothing parameter opens up new possibilities. It leads to asymptotically equivalent reproducing kernel estimators (without qualifications), and thence, via uniform error bounds for kernel estimators, to uniform rror bounds for smoothing splines and via strong approximations, to confidence bands for the unknown regression function. The reason for studying smoothing splines of arbitrary order is that one wants to use them for data analysis.

Regarding the actual computation, the usual scheme based on spline interpolation is useful for cubic smoothing splines only. For splines of arbitrary order, the Kalman filter is the most important method, the intricacies of which are explained in full. The authors also discuss simulation results for smoothing splines and local and global polynomials for a variety of test problems as well as results on confidence bands for the unknown regression function based on undersmoothed quintic smoothing splines with remarkably good coverage probabilities.

Quasi-Likelihood and Its Application

Автор: Heyde
Название: Quasi-Likelihood and Its Application
ISBN: 0387982256 ISBN-13(EAN): 9780387982250
Издательство: Springer
Цена: 20789 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Quasi-likelihood is a generally applicable estimating function based methodology for optimally estimating model parameters in systems subject to random effects. This book gives an account of the essential features of quasi-likelihood methodology, and stresses its value as a general purpose inferential tool.

Likelihood, Bayesian and MCMC Methods in Quantitative Genetics

Автор: Sorensen Daniel, Gianola Daniel
Название: Likelihood, Bayesian and MCMC Methods in Quantitative Genetics
ISBN: 0387954406 ISBN-13(EAN): 9780387954400
Издательство: Springer
Цена: 41579 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Over the last ten years the introduction of computer intensive statistical methods has opened new horizons concerning the probability models that can be fitted to genetic data, the scale of the problems that can be tackled and the nature of the questions that can be posed. In particular, the application of Bayesian and likelihood methods to statistical genetics has been facilitated enormously by these methods. Techniques generally referred to as Markov chain Monte Carlo (MCMC) have played a major role in this process, stimulating synergies among scientists in different fields, such as mathematicians, probabilists, statisticians, computer scientists and statistical geneticists. Specifically, the MCMC "revolution" has made a deep impact in quantitative genetics. This can be seen, for example, in the vast number of papers dealing with complex hierarchical models and models for detection of genes affecting quantitative or meristic traits in plants, animals and humans that have been published recently. This book, suitable for numerate biologists and for applied statisticians, provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Most students in biology and agriculture lack the formal background needed to learn these modern biometrical techniques. Although a number of excellent texts in these areas have become available in recent years, the basic ideas and tools are typically described in a technically demanding style, and have been written by and addressed to professional statisticians. For this reason, considerable more detail is offered than what may be warranted for a more mathematically apt audience. The book is divided into four parts. Part I gives a review of probability and distribution theory. Parts II and III present methods of inference and MCMC methods. Part IV discusses several models that can be applied in quantitative genetics, primarily from a Bayesian perspective. An effort has been made to relate biological to statistical parameters throughout, and examples are used profusely to motivate the developments. Daniel Sorensen is a Research Professor in Statistical Genetics, at the Department of Animal Breeding and Genetics in the Danish Institute of Agricultural Sciences. Daniel Gianola is Professor in the Animal Sciences, Biostatistics and Medical Informatics, and Dairy Science Departments of the University of Wisconsin-Madison. Gianola and Sorensen pioneered the introduction of Bayesian and MCMC methods in animal breeding. The authors have published and lectured extensively in applications of statistics to quantitative genetics.

Empirical Bayes and Likelihood Inference

Автор: Ahmed S.E., Reid N.
Название: Empirical Bayes and Likelihood Inference
ISBN: 0387950184 ISBN-13(EAN): 9780387950181
Издательство: Springer
Цена: 14849 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Bayesian and likelihood approaches to inference have a number of points of close contact, especially from an asymptotic point of view. Both approaches emphasize the construction of interval estimates of unknown parameters. Empirical Bayes methods have historically emphasized instead the construction of point estimates. In this volume researchers present recent work on several aspects of Bayesian, likelihood and empirical Bayes methods, presented at a workshop held in Montreal, Canada. The goal of the workshop was to explore the linkages among the methods, and to suggest new directions for research in the theory of inference.

Likelihood-based Inference on Cointegrated Vector Autoregressive Models

Автор: Johansen, Soren (Professor, Institute of Mathemati
Название: Likelihood-based Inference on Cointegrated Vector Autoregressive Models
ISBN: 0198774508 ISBN-13(EAN): 9780198774501
Издательство: Oxford Academ
Цена: 11931 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Professor Johansen gives a detailed mathematical and statistical analysis of the co-integrated vector autoregressive model in a self-contained presentation for graduate students and researchers with a good knowledge of multivariate regression analysis and likelihood methods. Many exercises are provided.

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