Name: Estimation and Inference in Nonparametric Frontier Models: Recent Developments and Perspectives (en Inglés)
Price: 413.27 PEN
Availability: InStock
Author: Simar, Leopold ; Wilson, Paul W.
ISBN: 9781601986665

portada Estimation and Inference in Nonparametric Frontier Models: Recent Developments and Perspectives (en Inglés)

Formato

Libro Físico

Autor

Paul W. Wilson

Léopold Simar

Editorial

Now Publishers

Idioma

Inglés

N° páginas

170

Encuadernación

Tapa Blanda

Dimensiones

23.4 x 15.6 x 0.9 cm

Peso

0.24 kg.

ISBN13

9781601986665

Categorías

Econometría

Estimation and Inference in Nonparametric Frontier Models: Recent Developments and Perspectives (en Inglés)

Paul W. Wilson (Autor) · Léopold Simar (Autor) · Now Publishers · Tapa Blanda

Libro Nuevo

S/ 413,27

S/ 826,53

Ahorras: S/ 413,27

50% descuento

Envío normal

Origen: Estados Unidos (Costos de importación incluídos en el precio)

Se enviará desde nuestra bodega entre el Lunes 12 de Agosto y el Lunes 26 de Agosto.

Lo recibirás en cualquier lugar de Perú entre 2 y 5 días hábiles luego del envío.

Reseña del libro "Estimation and Inference in Nonparametric Frontier Models: Recent Developments and Perspectives (en Inglés)"

Estimation and Inference in Nonparametric Frontier Models provides a thorough examination of this topic for students and researchers alike. While nonparametric estimators are widely used to estimate the productive efficiency of firms and other organizations, it is often done without any attempt to make statistical inference. Recent work has provided statistical properties of these estimators and methods for making statistical inference has established a link between frontier estimation and extreme value theory. New estimators that avoid many of the problems inherent with traditional efficiency estimators have been developed and these new estimators are robust with respect to outliers and avoid the problem of dimensionality. In addition, statistical properties, including asymptotic distributions, of the new estimators are uncovered. Finally, the authors show several approaches for introducing environmental variables into production models.