The Effect of Leap Years and Seasonal Trends on the Birthday Problem

Most data have a space and time label associated with them; data that are close together are usually more correlated than those that are far apart. Prediction (or forecasting) of a process at a particular label where there is no datum, from observed nearby data, is the subject of this article. One approach, known as geostatistics, is featured, from which linear methods of spatial prediction (kriging) will be considered. Brief reference is made to other linear/nonlinear, stochastic/deterministic predictors. The (linear) geostatistical method is applied to piezometric-head data around a potential nuclear-waste repository site.