Period analysis of variable stars by robust smoothing

The objective of this paper 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 (RCV) induced by a robust smoothing spline regression. Once the period is determined, a nonparametric method is used to estimate the light curve. A real example and a simulation study suggest that RCV and GCV are superior to existing methods.