Описание: Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes methods.
Автор: Azzalini, Adelchi Название: Statistical Inference Based on the likelihood ISBN: 041260650X ISBN-13(EAN): 9780412606502 Издательство: Taylor&Francis Рейтинг: Цена: 19906.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Aitkin, Murray Название: Statistical Inference ISBN: 0367383942 ISBN-13(EAN): 9780367383947 Издательство: Taylor&Francis Рейтинг: Цена: 9798.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical methods for hypothesis testing.
After an overview of the competing theories of statistical inference, the book introduces the Bayes/likelihood approach used throughout. It presents Bayesian versions of one- and two-sample t-tests, along with the corresponding normal variance tests. The author then thoroughly discusses the use of the multinomial model and noninformative Dirichlet priors in "model-free" or nonparametric Bayesian survey analysis, before covering normal regression and analysis of variance. In the chapter on binomial and multinomial data, he gives alternatives, based on Bayesian analyses, to current frequentist nonparametric methods. The text concludes with new goodness-of-fit methods for assessing parametric models and a discussion of two-level variance component models and finite mixtures.
Emphasizing the principles of Bayesian inference and Bayesian model comparison, this book develops a unique methodology for solving challenging inference problems. It also includes a concise review of the various approaches to inference.
Автор: P.P.B. Eggermont; V.N. LaRiccia Название: Maximum Penalized Likelihood Estimation ISBN: 0387952683 ISBN-13(EAN): 9780387952680 Издательство: Springer Рейтинг: Цена: 25853.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints such as unimodality and log-concavity. This book focuses on convexity and convex optimization, as applied to maximum penalized likelihood estimation.
Автор: Paul P. Eggermont; Vincent N. LaRiccia Название: Maximum Penalized Likelihood Estimation ISBN: 1461417120 ISBN-13(EAN): 9781461417125 Издательство: Springer Рейтинг: Цена: 23058.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Ideal for researchers and practitioners in statistics and industrial mathematics, this book covers the theory and practice of nonparametric estimation. It is novel in its use of maximum penalized likelihood estimation and convex minimization problem theory.
Автор: Nico J.D. Nagelkerke Название: Maximum Likelihood Estimation of Functional Relationships ISBN: 038797721X ISBN-13(EAN): 9780387977218 Издательство: Springer Рейтинг: Цена: 16070.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The theory of functional relationships concerns itself with inference from models with a more complex error structure than those existing in regression models. In this monograph we will explore the properties of likelihood methods in the context of functional relationship models.
Автор: P.P.B. Eggermont; V.N. LaRiccia Название: Maximum Penalized Likelihood Estimation ISBN: 1441929282 ISBN-13(EAN): 9781441929280 Издательство: Springer Рейтинг: Цена: 25853.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints.
Автор: Schweder Название: Confidence, Likelihood, Probability ISBN: 0521861608 ISBN-13(EAN): 9780521861601 Издательство: Cambridge Academ Рейтинг: Цена: 12514.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This is the first book to develop a methodology of confidence distributions, with a lively mix of theory, illustrations, applications and exercises.
Автор: Aitkin, Murray (university Of Melbourne, Australia) Название: Statistical inference ISBN: 1420093436 ISBN-13(EAN): 9781420093438 Издательство: Taylor&Francis Рейтинг: Цена: 17609.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Sets out an integrated approach to statistical inference using the likelihood function as the primary measure of evidence for statistical model parameters, and for the statistical models themselves. This book provides both an alternative to standard Bayesian inference and the foundation for a course sequence in modern Bayesian theory.
Автор: Daniel Sorensen; Daniel Gianola Название: Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics ISBN: 1441929975 ISBN-13(EAN): 9781441929976 Издательство: Springer Рейтинг: Цена: 26120.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Автор: Tiwari, Ram (us Food And Drug Administration, Silver Spring, Md) Zalkikar, Jyoti (us Food And Drug Administration, Silver Spring, Md) Huang, Lan (us F Название: Signal detection for medical scientists ISBN: 0367201437 ISBN-13(EAN): 9780367201432 Издательство: Taylor&Francis Рейтинг: Цена: 17609.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents the data mining techniques with focus on likelihood ratio test (LRT) based methods for signal detection. It emphasizes computational aspect of LRT methodology and is pertinent for first-time researchers and graduate students venturing into this interesting field.
Автор: Carlos A. Coelho; Barry C. Arnold Название: Finite Form Representations for Meijer G and Fox H Functions ISBN: 3030287890 ISBN-13(EAN): 9783030287894 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book depicts a wide range of situations in which there exist finite form representations for the Meijer G and the Fox H functions. Accordingly, it will be of interest to researchers and graduate students who, when implementing likelihood ratio tests in multivariate analysis, would like to know if there exists an explicit manageable finite form for the distribution of the test statistics. In these cases, both the exact quantiles and the exact p-values of the likelihood ratio tests can be computed quickly and efficiently.The test statistics in question range from common ones, such as those used to test e.g. the equality of means or the independence of blocks of variables in real or complex normally distributed random vectors; to far more elaborate tests on the structure of covariance matrices and equality of mean vectors. The book also provides computational modules in Mathematica®, MAXIMA and R, which allow readers to easily implement, plot and compute the distributions of any of these statistics, or any other statistics that fit into the general paradigm described here.
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