  We consider nonparametric estimation of the derivative of a probability
density function with the bounded support on $[0,\infty)$. Estimates are looked
up in the class of estimates with asymmetric gamma kernel functions. The use of
gamma kernels is due to the fact they are nonnegative, change their shape
depending on the position on the semi-axis and possess other good properties.
We found analytical expressions for bias, variance, mean integrated squared
error (MISE) of the derivative estimate. An optimal bandwidth, the optimal
MISE, and rate of mean square convergence of the estimates for density
derivative have also been found.
