  The super-parametric density estimators and its related algorism were
suggested by Y. -S. Tsai et al [7]. The number of parameters is unlimited in
the super- parametric estimators and it is a general theory in sense of
unifying or connecting nonparametric and parametric estimators. Before applying
to numerical examples, we can not give any comment of the estimators. In this
paper, we will focus on the implementation, the computer programming, of the
algorism and strategies of choosing window functions. B-splines, Bezier splines
and covering windows are studied as well. According to the criterion of the
convergence conditions for Parzen window, the number of the window functions
shall be, roughly, proportional to the number of samples and so is the number
of the variables. Since the algorism is designed for solving the optimization
of likelihood function, there will be a set of nonlinear equations with a large
number of variables. The results show that algorism suggested by Y. -S. Tsai is
very powerful and effective in the sense of mathematics, that is, the iteration
procedures converge and the rates of convergence are very fast. Also, the
numerical results of different window functions show that the approach of
super-parametric density estimators has ushered a new era of statistics.
