  We explore different estimators of the local non-linear coupling parameter,
fNL, based on the binned bispectrum presented in Bucher et al. Using
simulations of Wilkinson Microwave Anisotropy Probe (WMAP)-7yr data, we compare
the performance of a regression neural network with a \chi^2-minimization and
study the dependence of the results on the presence of the linear term in the
analysis and on the use of inpainting for masked regions. Both methods obtain
similar results and are robust to the use of inpainting, but the neural network
estimator converges considerably faster. We also examine the performance of a
simplified \chi^2 estimator that assumes a diagonal matrix and has the linear
term subtracted, which considerably reduces the computational time; in this
case inpainting is found to be crucial. The estimators are also applied to real
WMAP-7yr data, yielding constraints at 95% confidence level of -3< fNL <83.
