Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/260038 
Authors: 
Year of Publication: 
2012
Series/Report no.: 
Working Paper No. 2012:12
Publisher: 
Lund University, School of Economics and Management, Department of Economics, Lund
Abstract: 
Detection turning points in unimodel has various applications to time series which have cyclic periods. Related techniques are widely explored in the field of statistical surveillance, that is, on-line turning point detection procedures. This paper will first present a power controlled turning point detection method based on the theory of the likelihood ratio test in statistical surveillance. Next we show how outliers will influence the performance of this methodology. Due to the sensitivity of the surveillance system to outliers, we finally present a wavelet multiresolution (MRA) based outlier elimination approach, which can be combined with the on-line turning point detection process and will then alleviate the false alarm problem introduced by the outliers.
Subjects: 
Unimodel
Turning point
Statistical Surveillance
Outlier
Wavelet multiresolution
Threshold
JEL: 
C12
C52
C63
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.