Articles liés à Parametric Bootstrap for Linear Regression with Long-memory...

Parametric Bootstrap for Linear Regression with Long-memory Errors: An Improvement to the Traditional Delta Method Approach - Couverture souple

Aga, Mosisa

 
9783838340616: Parametric Bootstrap for Linear Regression with Long-memory Errors: An Improvement to the Traditional Delta Method Approach
Couverture souple
ISBN 10 : 3838340612 ISBN 13 : 9783838340616
Editeur : LAP Lambert Academic Publishing, 2010

Synopsis

Invented in 1979 by Bradley Efron, the relatively new topic of bootstrap approximation technique is becoming one of the most efficient and fast expanding methods of statistical analysis, used not only by statisticians, but also by other researchers in economics, finance, medical sciences, life sciences, social sciences, and business. However, the current application of bootstrap is largely focused on independent and identically distributed (iid) data and to a lesser extent on weakly dependent data structures. Very little attempt is done to analyze the performance of bootstrap to strongly dependent (long-memory) processes. This work aims at laying the mathematical foundation for the application of parametric bootstrap to regression processes whose disturbance terms are strongly dependent. It is shown that, under some sets of conditions on the regression coefficients, the spectral density function, and the parameter values, the parametric bootstrap based on the plug-in log-likelihood (PLL) function of linear regression processes with Gaussian, stationary, and long-memory errors, provides higher-order improvements over the traditional delta method.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Invented in 1979 by Bradley Efron, the relatively new topic of bootstrap approximation technique is becoming one of the most efficient and fast expanding methods of statistical analysis, used not only by statisticians, but also by other researchers in economics, finance, medical sciences, life sciences, social sciences, and business. However, the current application of bootstrap is largely focused on independent and identically distributed (iid) data and to a lesser extent on weakly dependent data structures. Very little attempt is done to analyze the performance of bootstrap to strongly dependent (long-memory) processes. This work aims at laying the mathematical foundation for the application of parametric bootstrap to regression processes whose disturbance terms are strongly dependent. It is shown that, under some sets of conditions on the regression coefficients, the spectral density function, and the parameter values, the parametric bootstrap based on the plug-in log-likelihood (PLL) function of linear regression processes with Gaussian, stationary, and long-memory errors, provides higher-order improvements over the traditional delta method.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP Lambert Academic Publishing
Date d'édition
2010
Langue
anglais
ISBN 10 
3838340612
ISBN 13 
9783838340616
Reliure
Broché
Nombre de pages
64
Coordonnées du fabricant
non disponible
Personne responsable
KreGa Onlinehandel GmbH
4950000000000
info@krega-onlinehandel.de

Birkenweg 31
Brakel
33034
Allemagne