Skip to main content

Period analysis of variable stars by robust smoothing

Buy Article:

$48.00 plus tax (Refund Policy)

Abstract:

Summary. 

The objective is to estimate the period and the light curve (or periodic function) of a variable star. Previously, several methods have been proposed to estimate the period of a variable star, but they are inaccurate especially when a data set contains outliers. We use a smoothing spline regression to estimate the light curve given a period and then find the period which minimizes the generalized cross-validation (GCV). The GCV method works well, matching an intensive visual examination of a few hundred stars, but the GCV score is still sensitive to outliers. Handling outliers in an automatic way is important when this method is applied in a ‘data mining’ context to a vary large star survey. Therefore, we suggest a robust method which minimizes a robust cross-validation criterion induced by a robust smoothing spline regression. Once the period has been determined, a nonparametric method is used to estimate the light curve. A real example and a simulation study suggest that the robust cross-validation and GCV methods are superior to existing methods.

Keywords: Generalized cross-validation; Period; Periodic function; Robust spline regression; Smoothing spline regression

Document Type: Research Article

DOI: http://dx.doi.org/10.1111/j.1467-9876.2004.00423.x

Affiliations: 1: University of Alberta, Edmonton, Canada. 2: National Center for Atmospheric Research, Boulder, USA.

Publication date: January 1, 2004

bpl/rssc/2004/00000053/00000001/art00002
dcterms_title,dcterms_description,pub_keyword
6
5
20
40
5

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
ingentaconnect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more