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In all likelihood, Pawitan, Yudi (professor Of Statistics, Department Of Statistics, National University Of Ireland, Cork)


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Автор: Pawitan, Yudi (professor Of Statistics, Department Of Statistics, National University Of Ireland, Cork)
Название:  In all likelihood
ISBN: 9780199671229
Издательство: Oxford Academ
Классификация:

ISBN-10: 0199671222
Обложка/Формат: Paperback
Страницы: 544
Вес: 0.81 кг.
Дата издания: 17.01.2013
Язык: English
Иллюстрации: 115 b/w line drawings
Размер: 233 x 159 x 28
Читательская аудитория: Tertiary education (us: college)
Подзаголовок: Statistical modelling and inference using likelihood
Ссылка на Издательство: Link
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Поставляется из: Англии

Likelihood and Bayesian Inference: With Applications in Biology and Medicine

Автор: Held Leonhard, Sabanйs Bovй Daniel
Название: Likelihood and Bayesian Inference: With Applications in Biology and Medicine
ISBN: 3662607913 ISBN-13(EAN): 9783662607916
Издательство: Springer
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Цена: 53100.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes methods.

Statistical Inference Based on the likelihood

Автор: Azzalini, Adelchi
Название: Statistical Inference Based on the likelihood
ISBN: 041260650X ISBN-13(EAN): 9780412606502
Издательство: Taylor&Francis
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Цена: 132710.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.

Statistical Inference

Автор: Aitkin, Murray
Название: Statistical Inference
ISBN: 0367383942 ISBN-13(EAN): 9780367383947
Издательство: Taylor&Francis
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Цена: 65320.00 T
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.


Local Regression and Likelihood

Автор: Clive Loader
Название: Local Regression and Likelihood
ISBN: 1475772580 ISBN-13(EAN): 9781475772586
Издательство: Springer
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Цена: 149060.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Separation of signal from noise is the most fundamental problem in data analysis, arising in such fields as: signal processing, econometrics, actuarial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, with extensions to local likelihood and density estimation.

Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics

Автор: Daniel Sorensen; Daniel Gianola
Название: Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics
ISBN: 1441929975 ISBN-13(EAN): 9781441929976
Издательство: Springer
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Цена: 174130.00 T
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.

Maximum Penalized Likelihood Estimation

Автор: P.P.B. Eggermont; V.N. LaRiccia
Название: Maximum Penalized Likelihood Estimation
ISBN: 0387952683 ISBN-13(EAN): 9780387952680
Издательство: Springer
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Цена: 172350.00 T
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.

Maximum Penalized Likelihood Estimation

Автор: Paul P. Eggermont; Vincent N. LaRiccia
Название: Maximum Penalized Likelihood Estimation
ISBN: 1461417120 ISBN-13(EAN): 9781461417125
Издательство: Springer
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Цена: 153720.00 T
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.

Maximum Likelihood Estimation of Functional Relationships

Автор: Nico J.D. Nagelkerke
Название: Maximum Likelihood Estimation of Functional Relationships
ISBN: 038797721X ISBN-13(EAN): 9780387977218
Издательство: Springer
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Цена: 107130.00 T
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.

Signal detection for medical scientists

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: 117390.00 T
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.

Maximum Penalized Likelihood Estimation

Автор: P.P.B. Eggermont; V.N. LaRiccia
Название: Maximum Penalized Likelihood Estimation
ISBN: 1441929282 ISBN-13(EAN): 9781441929280
Издательство: Springer
Рейтинг:
Цена: 172350.00 T
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.

Confidence, Likelihood, Probability

Автор: Schweder
Название: Confidence, Likelihood, Probability
ISBN: 0521861608 ISBN-13(EAN): 9780521861601
Издательство: Cambridge Academ
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Цена: 83430.00 T
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.

Statistical inference

Автор: Aitkin, Murray (university Of Melbourne, Australia)
Название: Statistical inference
ISBN: 1420093436 ISBN-13(EAN): 9781420093438
Издательство: Taylor&Francis
Рейтинг:
Цена: 117390.00 T
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.


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