Prediction intervals with a Dirichlet‐process prior distribution
Gregory Campbell, Myles Hollander · Canadian Journal of Statistics · 1982
Abstract Let X1, …,Xn, and Y1, … Yn be consecutive samples from a distribution function F which itself is randomly chosen according to the Ferguson (1973) Dirichlet‐process prior distribution on the space of distribution functions. Typically, prediction intervals employ the observations X1,…, Xn in the first sample in order to predict a specified function of the future sample Y1, …, Yn. Here one‐ and two‐sided prediction intervals for at least q of N future observations are developed for the situation in which, in addition to the previous sample, there is prior information available. The information is specified via the parameter α of the Dirichlet process prior distribution.