In this paper we apply recently developed nonparametric kernel regression estimation methods to quantify the conditional distribution of motion-picture earnings. The nonparametric data-driven approach allows the full range of relationships among variables to be captured, including nonlinearities that usually remain hidden in parametric models. The nonparametric approach does not assume a functi...

In recent years, some investigations have been carried out to examine the assumptions like stationarity, symmetry and separability of spatio-temporal covariance function which would considerably simplify fitting a valid covariance model to the data by parametric and nonparametric methods. In this article, assuming a Gaussian random field, we consider the likelihood ratio separability test, a va...