estimation inference specification analysis von white halbert (7 Ergebnisse)

Hardcover. Zustand: Good. No Jacket. Condition : Good. Former-university library copy with associated library stamps etc. Hard cover, no jacket. 380pp. No highlighting or annotations to text. Please note the image is a stock photo - not the actual copy.

Zustand: New. In.

Paperback. Zustand: Brand New. reprint edition. 392 pages. 9.00x6.25x0.75 inches. In Stock.

Zustand: New. In.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models, and gives the conditions under which parameters of interest can be consistently estimated despite misspecification, and the consequences of misspecification, for hypothesis testing in estimating the asymptotic covariance matrix of the parameters. Although the theory presented in the book is motivated by econometric problems, its applicability is by no means restricted to economics. Subject to defined limitations, the theory applies to any scientific context in which statistical analysis is conducted using approximate models.

Zustand: New. This book examines the consequences of misspecifications from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference. Series Editor(s): Chesher, Andrew; Jackson, Matthew O. Series: Econometric Society Monographs. Num Pages: 396 pages, black & white illustrations. BIC Classification: KCH; PBT. Category: (P) Professional & Vocational. Dimension: 228 x 152 x 25. Weight in Grams: 641. . 1994. hardcover. . . . . Books ship from the US and Ireland.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models, and gives the conditions under which parameters of interest can be consistently estimated despite misspecification, and the consequences of misspecification, for hypothesis testing in estimating the asymptotic covariance matrix of the parameters. Although the theory presented in the book is motivated by econometric problems, its applicability is by no means restricted to economics. Subject to defined limitations, the theory applies to any scientific context in which statistical analysis is conducted using approximate models.