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Many photometric time-domain surveys are driven by specific goals, such assearches for supernovae or transiting exoplanets, which set the cadence withwhich fields are re-imaged. In the case of the Palomar Transient Factory (PTF),several sub-surveys are conducted in parallel, leading to non-uniform samplingover its $\sim$$20,000 \mathrm{deg}^2$ footprint. While the median $7.26\mathrm{deg}^2$ PTF field has been imaged $\sim$40 times in \textit{R}-band,$\sim$$2300 \mathrm{deg}^2$ have been observed $>$100 times. We use PTF data tostudy the trade-off between searching for microlensing events in a survey whosefootprint is much larger than that of typical microlensing searches, but withfar-from-optimal time sampling. To examine the probability that microlensingevents can be recovered in these data, we test statistics used on uniformlysampled data to identify variables and transients. We find that the von Neumannratio performs best for identifying simulated microlensing events in our data.We develop a selection method using this statistic and apply it to data fromfields with $>$10 $R$-band observations, $1.1\times10^9$ light curves,uncovering three candidate microlensing events. We lack simultaneous,multi-color photometry to confirm these as microlensing events. However, theirnumber is consistent with predictions for the event rate in the PTF footprintover the survey's three years of operations, as estimated from near-fieldmicrolensing models. This work can help constrain all-sky event ratepredictions and tests microlensing signal recovery in large data sets, whichwill be useful to future time-domain surveys, such as that planned with theLarge Synoptic Survey Telescope.

Statistical Searches for Microlensing Events in Large, Non-Uniformly Sampled Time-Domain Surveys: A Test Using Palomar Transient Factory Data