Parametric Statistical Models and Likelihood, Ole E Barndorff-Nielsen

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

Автор: Owen
Название: Empirical Likelihood
ISBN: 1584880716 ISBN-13(EAN): 9781584880714
Издательство: Taylor&Francis
Рейтинг:
Цена: 137810.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Applies empirical likelihood method to problems ranging 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.

Автор: Charles A. Rohde
Название: Introductory Statistical Inference with the Likelihood Function
ISBN: 3319374818 ISBN-13(EAN): 9783319374819
Издательство: Springer
Рейтинг:
Цена: 55890.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This textbook covers the fundamentals of statistical inference and statistical theory including Bayesian and frequentist approaches and methodology possible without excessive emphasis on the underlying mathematics. The likelihood function is used for pure likelihood inference throughout the book.

Автор: J.K. Ghosh; D. Basu
Название: Statistical Information and Likelihood
ISBN: 0387967516 ISBN-13(EAN): 9780387967516
Издательство: Springer
Рейтинг:
Цена: 111790.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: It is an honor to be asked to write a foreword to this book, for I believe that it and other books to follow will eventually lead to a dramatic change in the current statistics curriculum in our universities. My purpose was to complete a book on Statistical Reliability Theory with Frank Proschan.

Автор: Yan Liu; Fumiya Akashi; Masanobu Taniguchi
Название: Empirical Likelihood and Quantile Methods for Time Series
ISBN: 9811001510 ISBN-13(EAN): 9789811001512
Издательство: Springer
Рейтинг:
Цена: 51230.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book integrates the fundamentals of asymptotic theory of statistical inference for time series under nonstandard settings, e.g., infinite variance processes, not only from the point of view of efficiency but also from that of robustness and optimality by minimizing prediction error.

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: 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: 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.