Articles liés à Large Sample Inference for Long Memory Processes

Large Sample Inference for Long Memory Processes - Couverture rigide

Giraitis, Liudas; Koul, Hira L.; Surgailis, Donatas

 
9781848162785: Large Sample Inference for Long Memory Processes
Couverture rigide
ISBN 10 : 1848162782 ISBN 13 : 9781848162785
Editeur : Imperial College Press, 2012

Synopsis

A discrete-time stationary stochastic process with finite variance is said to have long memory if its autocorrelations tend to zero hyperbolically in the lag, i.e. like a power of the lag, as the lag tends to infinity. The absolute sum of autocorrelations of such processes diverges and their spectral density at the origin is unbounded. This is unlike the so-called weakly dependent processes, where autocorrelations tend to zero exponentially fast and the spectral density is bounded at the origin. In a long memory process, the dependence between the current observation and the one at a distant future is persistent; whereas in the weakly dependent processes, these observations are approximately independent. This fact alone is enough to warn a person about the validity of the classical inference procedures based on the square root of the sample size standardization when data are generated by a long-term memory process.The aim of this volume is to provide a text at the graduate level from which one can learn, in a concise fashion, some basic theory and techniques of proving limit theorems for numerous statistics based on long memory processes. It also provides a guide to researchers about some of the inference problems under long memory.

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

Présentation de l'éditeur

A discrete-time stationary stochastic process with finite variance is said to have long memory if its autocorrelations tend to zero hyperbolically in the lag, i.e. like a power of the lag, as the lag tends to infinity. The absolute sum of autocorrelations of such processes diverges and their spectral density at the origin is unbounded. This is unlike the so-called weakly dependent processes, where autocorrelations tend to zero exponentially fast and the spectral density is bounded at the origin. In a long memory process, the dependence between the current observation and the one at a distant future is persistent; whereas in the weakly dependent processes, these observations are approximately independent. This fact alone is enough to warn a person about the validity of the classical inference procedures based on the square root of the sample size standardization when data are generated by a long-term memory process. The aim of this volume is to provide a text at the graduate level from which one can learn, in a concise fashion, some basic theory and techniques of proving limit theorems for numerous statistics based on long memory processes. It also provides a guide to researchers about some of the inference problems under long memory.

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

Éditeur
Imperial College Press
Date d'édition
2012
Langue
anglais
ISBN 10 
1848162782
ISBN 13 
9781848162785
Reliure
Relié
Nombre de pages
577
Coordonnées du fabricant
non disponible
Personne responsable
gpsr@libri.de
gpsr@libri.de

Europaallee 1
Bad Hersfeld
36244
Allemagne