In the conventional approach to these questions the demand analyst chooses a parametric form for the underlying utility function and derives the associated set of parametric demand functions, which are then fitted to the data using some econometric technique. The resulting parameter estimates can then be tested to see if they conform to the restrictions imposed by the utility maximization model...

Non-parametric and computational methods of option pricing have recently attracted attention of researchers. These typically include highly data intensive, model-free approaches that complement traditional parametric methods. Non-parametric and computational methods of option pricing typically include highly data intensive, model-free approaches that complement traditional parametric methods. O...

One key issue in several astrophysical problems is the evaluation of the density probability function underlying an observational discrete data set. We here review two non-parametric density estimators which recently appeared in the astrophysical literature, namely the adaptive kernel density estimator and the Maximum Penalized Likelihood technique, and describe another method based on the wave...

In psychological research sometimes non-parametric procedures are in use in cases, where the corresponding parametric procedure is preferable. This is mainly due to the fact that we pay too much attention to the possible violation of the normality assumption which is needed to derive the exact distribution of the statistic used in the parametric approach. An example is the t-test and its non-pa...