Intended to provide the reader with a firm conceptual and empirical understanding of basic information-theoretic econometric models and methods.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
George G. Judge is a Professor at the University of California, Berkeley. Professor Judge has also served on the faculty of the University of Illinois, University of Connecticut, and Oklahoma State University and has been a visiting professor at several US and European universities. He is the coauthor or editor of 15 books in econometrics and related fields and author or coauthor of more than 150 articles in refereed journals. His research explores specification and evaluation of statistical decision rules, improved inference methods, and parametric and semiparametric estimation and information recovery in the case of ill-posed inverse problems with noise. Judge is a Fellow of the Econometric Society and the American Agricultural Economics Association.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Zustand: New. In English. Artikel-Nr. ria9780521869591_new
Anzahl: Mehr als 20 verfügbar
Anbieter: Kennys Bookstore, Olney, MD, USA
Zustand: New. Intended to provide the reader with a firm conceptual and empirical understanding of basic information-theoretic econometric models and methods. Num Pages: 248 pages, 13 b/w illus. 7 tables. BIC Classification: KCH. Category: (U) Tertiary Education (US: College). Dimension: 228 x 152 x 18. Weight in Grams: 460. . 2012. Illustrated. hardcover. . . . . Books ship from the US and Ireland. Artikel-Nr. V9780521869591
Anzahl: Mehr als 20 verfügbar
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
Hardcover. Zustand: Brand New. 232 pages. 9.25x6.14x0.83 inches. In Stock. Artikel-Nr. x-0521869595
Anzahl: 2 verfügbar
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is intended to provide the reader with a firm conceptual and empirical understanding of basic information-theoretic econometric models and methods. Because most data are observational, practitioners work with indirect noisy observations and ill-posed econometric models in the form of stochastic inverse problems. Consequently, traditional econometric methods in many cases are not applicable for answering many of the quantitative questions that analysts wish to ask. After initial chapters deal with parametric and semiparametric linear probability models, the focus turns to solving nonparametric stochastic inverse problems. In succeeding chapters, a family of power divergence measure-likelihood functions are introduced for a range of traditional and nontraditional econometric-model problems. Finally, within either an empirical maximum likelihood or loss context, Ron C. Mittelhammer and George G. Judge suggest a basis for choosing a member of the divergence family. Artikel-Nr. 9780521869591
Anzahl: 1 verfügbar