The moment method is a well known technique, which uses a time series of the first 3 moments of a spectral line, to estimate the (discrete) mode parameters and . The method, contrary to Doppler imaging, also yields other interesting (continuous) parameters such as the inclination angle , or sin during its identification procedure. In this talk, we are not only interested in the estimation of these continuous parameters themselves but also in reliable estimates for their uncertainty.
We designed a statistical formalism for the moment method based on the so-called generalized estimating equations (GEE). This formalism attempts to estimate the uncertainty of the continuous parameters taking into account that the different moments of a line profile are correlated and - more importantly - that the uncertainty of the observed moments depends on the pulsation parameters. The latter property of the moment method makes the least-squares technique a poor choice to estimate the uncertainty of the continuous parameters. We implemented the GEE method in an attempt to construct a statistically founded mode identification method and did performance tests on artificial datasets and on a high-resolution spectroscopic dataset of the star Cephei. We report on our experiences with this method and outline our ideas for improvements of spectroscopic mode identification.

 
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