Stein Estimation

de

, ,

Éditeur :

Springer


Paru le : 2023-09-29

eBook Téléchargement , DRM LCP 🛈 DRM Adobe 🛈
Lecture en ligne (streaming)
52,74

Téléchargement immédiat
Dès validation de votre commande
Image Louise Reader présentation

Louise Reader

Lisez ce titre sur l'application Louise Reader.

Description
This book provides a self-contained introduction of Stein/shrinkage estimation for the mean vector of a multivariate normal distribution. The book begins with a brief discussion of basic notions and results from decision theory such as admissibility, minimaxity, and (generalized) Bayes estimation. It also presents Stein's unbiased risk estimator and the James-Stein estimator in the first chapter. In the following chapters, the authors consider estimation of the mean vector of a multivariate normal distribution in the known and unknown scale case when the covariance matrix is a multiple of the identity matrix and the loss is scaled squared error. The focus is on admissibility, inadmissibility, and minimaxity of (generalized) Bayes estimators, where particular attention is paid to the class of (generalized) Bayes estimators with respect to an extended Strawderman-type prior. For almost all results of this book, the authors present a self-contained proof. The book is helpful for researchers and graduate students in various fields requiring data analysis skills as well as in mathematical statistics.

Pages
130 pages
Collection
n.c
Parution
2023-09-29
Marque
Springer
EAN papier
9789819960767
EAN PDF
9789819960774

Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
13
Taille du fichier
1485 Ko
Prix
52,74 €
EAN EPUB
9789819960774

Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
13
Taille du fichier
14316 Ko
Prix
52,74 €

Suggestions personnalisées