We elaborate on a deconvolution method, used to estimate the empirical distribution of unknown parameters, as suggested recently by Efron (2013). It is applied to estimating the empirical distribution of the 'sampling probabilities' of m sampled items. The estimated empirical distribution is used to modify the Horvitz-Thompson estimator. The performance of the modified Horvitz-Thompson estimator is studied in two examples. In one example the sampling probabilities are estimated based on the number of visits until a response was obtained. The other example is based on real data from panel sampling, where in four consecutive months there are corresponding four attempts to interview each member in a panel. The sampling probabilities are estimated based on the number of successful attempts. We also discuss briefly, further applications of deconvolution, including estimation of False discovery rate.