  In the last few years there has been an enormous effort in parameter
estimation studies for different sources with the space based gravitational
wave detector, LISA. While these studies have investigated sources of differing
complexity, the one thing they all have in common is they assume continuous
data streams. In reality, the LISA data stream will contain gaps from such
possible events such as repointing of the satellite antennae, to discharging
static charge build up on the satellites, to disruptions due to micro-meteor
strikes. In this work we conduct a large scale Monte Carlo parameter estimation
simulation for galactic binaries assuming data streams containing gaps. As the
expected duration and frequency of the gaps are currently unknown, we have
decided to focus on gaps of approximately one hour, occurring either once per
day or once per week. We also study the case where, as well as the expected
periodic gaps, we have a data drop-out of one continuous week. Our results show
that for for galactic binaries, a gap of once per week introduces a bias of
between 0.5% and 1% in the estimation of parameters, for the most important
parameters such as the sky position, amplitude and frequency. This number rises
to between 3% and 7% for the case of one gap a day, and to between 4% and 9%
when we have one gap a day and a spurious gap of a week. A future study will
investigate the effect of data gaps on supermassive black hole binaries and
extreme mass ratio inspirals.
