Result: Weighted Dickey : Fuller processes for detecting stationarity

Title:
Weighted Dickey : Fuller processes for detecting stationarity
Authors:
STELAND, Ansgar
Source:
5th St. Petersburg workshop on simulation, St. Petersburg State University, St. Petersburg, Russia, 26 June-2 July 2005. Part IIJournal of statistical planning and inference. 137(12):4011-4030
Publisher Information:
Amsterdam; Lausanne; New York,NY: Elsevier Science, 2007.
Publication Year:
2007
Physical Description:
print, 1/2 p
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Computer science, Informatique, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Probabilités et statistiques, Probability and statistics, Théorie des probabilités et processus stochastiques, Probability theory and stochastic processes, Processus stochastiques, Stochastic processes, Statistiques, Statistics, Généralités, General topics, Inférence à partir de processus stochastiques; analyse des séries temporelles, Inference from stochastic processes; time series analysis, Applications, Fiabilité, test de durée de vie, contrôle de la qualité, Reliability, life testing, quality control, Approximation asymptotique, Asymptotic approximation, Aproximación asintótica, Autocorrélation, Autocorrelation, Autocorrelación, Carte contrôle, Control chart, Carta control, Décision statistique, Statistical decision, Decisión estadística, Estimation statistique, Statistical estimation, Estimación estadística, Fonctionnelle, Functional, Funciónal, Industrie, Industry, Industria, Ingénierie, Engineering, Ingeniería, Invariant, Invariante, Loi limite, Limit distribution, Ley límite, Méthode noyau, Kernel method, Método núcleo, Méthode statistique, Statistical method, Método estadístico, Méthode séquentielle, Sequential method, Método secuencial, Noyau(mathématiques), Kernels, Paramètre nuisance, Nuisance parameter, Parámetro daño, Processus innovation, Innovation process, Proceso innovación, Processus stochastique, Stochastic process, Proceso estocástico, Racine unitaire, Unit root, Raíz unitaria, Simulation, Simulación, Série temporelle, Time series, Serie temporal, Temps arrêt, Stopping time, Tiempo parada, Test hypothèse, Hypothesis test, Test hipótesis, Théorème central limite, Central limit theorem, Teorema central límite, Théorème limite, Limit theorem, Teorema límite, 60B12, 60F05, 60F17, 60F99, 60G40, 62M10, 62P30, Echantillon fini, Finite sample, Autoregressive unit root, Change point, Non-parametric smoothing, Robustness, Sequential analysis
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Institute of Statistics, RWTH Aachen University, Wüllnerstr. 3, 52056 Aachen, Germany
ISSN:
0378-3758
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=19061107
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.19061107
Database:
PASCAL Archive

Further Information

Aiming at monitoring a time series to detect stationarity as soon as possible, we introduce monitoring procedures based on kernel-weighted sequential Dickey-Fuller (DF) processes, and related stopping times, which may be called weighted DF control charts. Under rather weak assumptions, (functional) central limit theorems are established under the unit root null hypothesis and local-to-unity alternatives. For general dependent and heterogeneous innovation sequences the limit processes depend on a nuisance parameter. In this case of practical interest, one can use estimated control limits obtained from the estimated asymptotic law. Another easy-to-use approach is to transform the DF processes to obtain limit laws which are invariant with respect to the nuisance parameter. We provide asymptotic theory for both approaches and compare their statistical behavior in finite samples by simulation.