9780387945217 - Linear and Graphical Models: For The Multivariate Complex Normal Distribution: 101 (Lecture Notes in Statistics, 101) de Andersen, Heidi H. (8 resultados)

Trade Paperback. Condición: VG. used trade paperback edition. lightly shelfworn, corners perhaps slightly bumped. pages and binding are clean, straight and tight. there are no marks to the text or other serious flaws.

Softcover. Condición: Très bon. Ancien livre de bibliothèque avec équipements. Edition 1995. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 1995. Ammareal gives back up to 15% of this item's net price to charity organizations.

Condición: Gut. Zustand: Gut | Sprache: Englisch | Produktart: Bücher | In the last decade, graphical models have become increasingly popular as a statistical tool. This book is the first which provides an account of graphical models for multivariate complex normal distributions. Beginning with an introduction to the multivariate complex normal distribution, the authors develop the marginal and conditional distributions of random vectors and matrices. Then they introduce complex MANOVA models and parameter estimation and hypothesis testing for these models. After introducing undirected graphs, they then develop the theory of complex normal graphical models including the maximum likelihood estimation of the concentration matrix and hypothesis testing of conditional independence.

Condición: New. In.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - In the last decade, graphical models have become increasingly popular as a statistical tool. This book is the first which provides an account of graphical models for multivariate complex normal distributions. Beginning with an introduction to the multivariate complex normal distribution, the authors develop the marginal and conditional distributions of random vectors and matrices. Then they introduce complex MANOVA models and parameter estimation and hypothesis testing for these models. After introducing undirected graphs, they then develop the theory of complex normal graphical models including the maximum likelihood estimation of the concentration matrix and hypothesis testing of conditional independence.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In the last decade, graphical models have become increasingly popular as a statistical tool. This book is the first which provides an account of graphical models for multivariate complex normal distributions. Beginning with an introduction to the multivariate complex normal distribution, the authors develop the marginal and conditional distributions of random vectors and matrices. Then they introduce complex MANOVA models and parameter estimation and hypothesis testing for these models. After introducing undirected graphs, they then develop the theory of complex normal graphical models including the maximum likelihood estimation of the concentration matrix and hypothesis testing of conditional independence. 200 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. In the last decade, graphical models have become increasingly popular as a statistical tool. This book is the first which provides an account of graphical models for multivariate complex normal distributions. Beginning with an introduction to the multivaria.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In the last decade, graphical models have become increasingly popular as a statistical tool. This book is the first which provides an account of graphical models for multivariate complex normal distributions. Beginning with an introduction to the multivariate complex normal distribution, the authors develop the marginal and conditional distributions of random vectors and matrices. Then they introduce complex MANOVA models and parameter estimation and hypothesis testing for these models. After introducing undirected graphs, they then develop the theory of complex normal graphical models including the maximum likelihood estimation of the concentration matrix and hypothesis testing of conditional independence.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 200 pp. Englisch.