Publicado por Cambridge University Press, Cambridge, 2009
ISBN 10: 0521123259 ISBN 13: 9780521123259
Idioma: Inglés
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
EUR 71,14
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: new. Paperback. The theory of empirical processes provides valuable tools for the development of asymptotic theory in (nonparametric) statistical models, and makes possible the unified treatment of a number of them. This book reveals the relation between the asymptotic behaviour of M-estimators and the complexity of parameter space. Virtually all results are proved using only elementary ideas developed within the book; there is minimal recourse to abstract theoretical results. To make the results concrete, a detailed treatment is presented for two important examples of M-estimation, namely maximum likelihood and least squares. The theory also covers estimation methods using penalties and sieves. Many illustrative examples are given, including the Grenander estimator, estimation of functions of bounded variation, smoothing splines, partially linear models, mixture models and image analysis. Graduate students and professionals in statistics as well as those with an interest in applications, to such areas as econometrics, medical statistics, etc., will welcome this treatment. This book deals with estimation methods in statistics, and treats various models in a unified way. Many illustrative examples are given, including the Grenander estimator, smoothing splines, partially linear models, mixture models and image analysis. Graduate students and professionals in statistics will welcome this treatment. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Publicado por Cambridge University Press, Cambridge, 2009
ISBN 10: 0521123259 ISBN 13: 9780521123259
Idioma: Inglés
Librería: CitiRetail, Stevenage, Reino Unido
EUR 69,59
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: new. Paperback. The theory of empirical processes provides valuable tools for the development of asymptotic theory in (nonparametric) statistical models, and makes possible the unified treatment of a number of them. This book reveals the relation between the asymptotic behaviour of M-estimators and the complexity of parameter space. Virtually all results are proved using only elementary ideas developed within the book; there is minimal recourse to abstract theoretical results. To make the results concrete, a detailed treatment is presented for two important examples of M-estimation, namely maximum likelihood and least squares. The theory also covers estimation methods using penalties and sieves. Many illustrative examples are given, including the Grenander estimator, estimation of functions of bounded variation, smoothing splines, partially linear models, mixture models and image analysis. Graduate students and professionals in statistics as well as those with an interest in applications, to such areas as econometrics, medical statistics, etc., will welcome this treatment. This book deals with estimation methods in statistics, and treats various models in a unified way. Many illustrative examples are given, including the Grenander estimator, smoothing splines, partially linear models, mixture models and image analysis. Graduate students and professionals in statistics will welcome this treatment. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Publicado por Cambridge University Press, Cambridge, 2009
ISBN 10: 0521123259 ISBN 13: 9780521123259
Idioma: Inglés
Librería: AussieBookSeller, Truganina, VIC, Australia
EUR 91,56
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: new. Paperback. The theory of empirical processes provides valuable tools for the development of asymptotic theory in (nonparametric) statistical models, and makes possible the unified treatment of a number of them. This book reveals the relation between the asymptotic behaviour of M-estimators and the complexity of parameter space. Virtually all results are proved using only elementary ideas developed within the book; there is minimal recourse to abstract theoretical results. To make the results concrete, a detailed treatment is presented for two important examples of M-estimation, namely maximum likelihood and least squares. The theory also covers estimation methods using penalties and sieves. Many illustrative examples are given, including the Grenander estimator, estimation of functions of bounded variation, smoothing splines, partially linear models, mixture models and image analysis. Graduate students and professionals in statistics as well as those with an interest in applications, to such areas as econometrics, medical statistics, etc., will welcome this treatment. This book deals with estimation methods in statistics, and treats various models in a unified way. Many illustrative examples are given, including the Grenander estimator, smoothing splines, partially linear models, mixture models and image analysis. Graduate students and professionals in statistics will welcome this treatment. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.