In statistics, semiparametric regression includes regression models that combine parametric and nonparametric models. His current research focuses on nonparametric Bayesian modelling and statistical machine learning. Software is available for using both parametric and nonparametric kernel methods. Dirichlet processes are frequently used in "Bayesian nonparametric statistics". In nonparametric statistics, a kernel is a weighting function used in non-parametric estimation techniques. Computer packages for using parametric and nonparametric kernel methods are available online. This can be done using simple nonparametric estimators, such as kernel regression. In statistics, a semiparametric model is a statistical model that has parametric and nonparametric components. GRNN represents an improved technique in the neural networks based on the nonparametric regression. To do so, we can use a parametric heteroskedasticity model, or a nonparametric estimator. Methods for the calculation of PSD may be generally classified as nonparametric and parametric. Thus the estimation task is statistically harder in nonparametric models. In statistics, an additive model (AM) is a nonparametric regression method. The "AM" uses a one-dimensional smoother to build a restricted class of nonparametric regression models. For symmetric probability distributions the value of the nonparametric skew is 0.