  I present the Phase Distance Correlation (PDC) periodogram -- a new
periodicity metric, based on the Distance Correlation concept of G\'abor
Sz\'ekely. For each trial period PDC calculates the distance correlation
between the data samples and their phases. PDC requires adaptation of the
Sz\'ekely's distance correlation to circular variables (phases). The resulting
periodicity metric is best suited to sparse datasets, and it performs better
than other methods for sawtooth-like periodicities. These include Cepheid and
RR-Lyrae light curves, as well as radial velocity curves of eccentric
spectroscopic binaries. The performance of the PDC periodogram in other
contexts is almost as good as that of the Generalized Lomb-Scargle periodogram.
The concept of phase distance correlation can be adapted also to astrometric
data, and it has the potential to be suitable also for large evenly-spaced
datasets, after some algorithmic perfection.
