Maximum Likelihood Estimation for Sample Surveys, Chambers, Raymond L.
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 Рейтинг: 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.
Автор: Clive Loader Название: Local Regression and Likelihood ISBN: 1475772580 ISBN-13(EAN): 9781475772586 Издательство: Springer Рейтинг: 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.
Автор: Aitkin, Murray Название: Statistical Inference ISBN: 0367383942 ISBN-13(EAN): 9780367383947 Издательство: Taylor&Francis Рейтинг: 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.
Автор: Bohning, Dankmar Название: Meta-analysis of Binary Data Using Profile Likelihood ISBN: 1584886307 ISBN-13(EAN): 9781584886303 Издательство: Taylor&Francis Рейтинг: Цена: 163330.00 T Наличие на складе: Нет в наличии.
Автор: Royall, Richard Название: Statistical Evidence ISBN: 0412044110 ISBN-13(EAN): 9780412044113 Издательство: Taylor&Francis Рейтинг: Цена: 163330.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Автор: Nico J.D. Nagelkerke Название: Maximum Likelihood Estimation of Functional Relationships ISBN: 038797721X ISBN-13(EAN): 9780387977218 Издательство: Springer Рейтинг: 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.
Автор: P.P.B. Eggermont; V.N. LaRiccia Название: Maximum Penalized Likelihood Estimation ISBN: 0387952683 ISBN-13(EAN): 9780387952680 Издательство: Springer Рейтинг: 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.
Автор: Paul P. Eggermont; Vincent N. LaRiccia Название: Maximum Penalized Likelihood Estimation ISBN: 1461417120 ISBN-13(EAN): 9781461417125 Издательство: Springer Рейтинг: 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.
Автор: Azzalini, Adelchi Название: Statistical Inference Based on the likelihood ISBN: 1032478012 ISBN-13(EAN): 9781032478012 Издательство: Taylor&Francis Рейтинг: Цена: 47970.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Il Do Ha; Jong-Hyeon Jeong; Youngjo Lee Название: Statistical Modelling of Survival Data with Random Effects ISBN: 9811349010 ISBN-13(EAN): 9789811349010 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Нет в наличии. Описание: This book provides a groundbreaking introduction to the likelihood inference for correlated survival data via the hierarchical (or h-) likelihood in order to obtain the (marginal) likelihood and to address the computational difficulties in inferences and extensions.
Автор: Zhou, Mai Название: Empirical likelihood method in survival analysis ISBN: 0367377578 ISBN-13(EAN): 9780367377571 Издательство: Taylor&Francis Рейтинг: Цена: 65320.00 T Наличие на складе: Есть у поставщика 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.
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