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Period analysis of variable stars by robust smoothing
Author(s) -
Oh HeeSeok,
Nychka Doug,
Brown Tim,
Charbonneau Paul
Publication year - 2004
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2004.00423.x
Subject(s) - outlier , smoothing spline , smoothing , nonparametric statistics , spline (mechanical) , mathematics , nonparametric regression , context (archaeology) , statistics , cross validation , regression , computer science , engineering , spline interpolation , geography , structural engineering , archaeology , bilinear interpolation
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.