  Context. The determination of meteor shower or parent body associations is
inherently a statistical problem. Traditional methods, primarily the similarity
discriminants, have limitations, particularly in handling the increasing volume
and complexity of meteoroid orbit data.
  Aims. We aim to introduce a new, more statistically robust and generalizable
method for estimating false positive detections in meteor shower
identification, leveraging Kernel Density Estimation (KDE).
  Methods. Utilizing a dataset of 824 fireballs observed by the European
Fireball Network, we apply a multivariate Gaussian kernel within KDE and
z-score data normalization. Our method analyzes the parameter space of
meteoroid orbits and geocentric impact characteristics, focusing on four
different similarity discriminants: DSH, D', DH, and DN.
  Results. The KDE methodology consistently converges towards a true
established shower-associated fireball rate within the EFN dataset of 18-25%
for all criteria. This indicates that the approach provides a more
statistically robust estimate of the shower-associated component.
  Conclusions. Our findings highlight the potential of KDE, combined with
appropriate data normalization, in enhancing the accuracy and reliability of
meteor shower analysis. This method addresses the existing challenges posed by
traditional similarity discriminants and offers a versatile solution adaptable
to varying datasets and parameters.
