Book Description Condition: New. Book is in NEW condition. 0.5. Seller Inventory # 9819960762-2-1
Book Description Condition: New. New! This book is in the same immaculate condition as when it was published 0.5. Seller Inventory # 353-9819960762-new
Book Description Soft Cover. Condition: new. Seller Inventory # 9789819960767
Book Description Paperback or Softback. Condition: New. Stein Estimation 0.45. Book. Seller Inventory # BBS-9789819960767
Book Description Condition: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book. Seller Inventory # ria9789819960767_lsuk
Book Description Paperback. Condition: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9789819960767
Book Description Condition: New. In. Seller Inventory # ria9789819960767_new
Book Description Condition: New. Seller Inventory # I-9789819960767
Book Description Paperback. Condition: Brand New. 138 pages. 9.25x6.10x0.30 inches. In Stock. Seller Inventory # x-9819960762
Book Description Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 140 pp. Englisch. Seller Inventory # 9789819960767