  We provide a comprehensive multi-aspect study on the performance of a
pipeline used by the LIGO-Virgo Collaboration for estimating parameters of
gravitational-wave bursts. We add simulated signals with four different
morphologies (sine-Gaussians, Gaussians, white-noise bursts, and binary black
hole signals) to simulated noise samples representing noise of the two Advanced
LIGO detectors during their first observing run. We recover them with the
BayesWave (BW) pipeline to study its accuracy in sky localization, waveform
reconstruction, and estimation of model-independent waveform parameters. BW
localizes sources with a level of accuracy comparable for all four
morphologies, with the median separation of actual and estimated sky locations
ranging from 25.1$^{\circ}$ to 30.3$^{\circ}$. This is a reasonable accuracy in
the two-detector case, and is comparable to accuracies of other localization
methods studied previously. As BW reconstructs generic transient signals with
sine-Gaussian wavelets, it is unsurprising that BW performs the best in
reconstructing sine-Gaussian and Gaussian waveforms. BW's accuracy in waveform
reconstruction increases steeply with network signal-to-noise ratio (SNR$_{\rm
net}$), reaching a $85\%$ and $95\%$ match between the reconstructed and actual
waveform below SNR$_{\rm net} \approx 20$ and SNR$_{\rm net} \approx 50$,
respectively, for all morphologies. BW's accuracy in estimating central moments
of waveforms is only limited by statistical errors in the frequency domain, and
is affected by systematic errors too in the time domain as BW cannot
reconstruct low-amplitude parts of signals overwhelmed by noise. The figures of
merit we introduce can be used in future characterizations of parameter
estimation pipelines.
