Description: Nonparametric regression tries to ?nd a relationship between the predictor and the response without assuming shape of estimated regression function. Its theory and methods are well developed for the case of completely observed vectors of predictors and responses. In many applications, including actuarial, some components of the observed vectors may be missed. Ignoring missing data and using known methods of nonparametric regression may yield inconsistent estimation. Theoretical results on optimal nonparametric regression with missing data are presented, and an applied actuarial example is discussed.

Description: This paper outlines a method of parametric modeling that may be used to produce forecasts and forecast intervals that will convey a measure of the reliance that may be placed on the forecasts. Followed by 2 discussion papers submitted