The Gaussian approximation for generalized Friedman’s urn model with heterogeneous and unbalanced updating

Abstract

The Friedman’s urn model is a popular urn model which is widely used in many disciplines. In particular, it is extensively used in treatment allocation schemes in clinical trials. Its asymptotic properties have been studied by many researchers. In literature, it is usually assumed that the expected number of balls added at each stage is a constant in despite of what type of balls are selected, that is, the updating of the urn is assumed to be balanced. When it is not, the asymptotic property of the Friedman’s urn model is stated in the book of Hu and Rosenberger (2006) as one of open problems in the area of adaptive designs. In this paper, we show that both the urn composition process and the allocation proportion process can be approximated by a multi-dimensional Gaussian process almost surely for a general multi-color Friedman type urn model with heterogeneous and unbalanced updating. The Gaussian process is a solution of a stochastic differential equation. As an application, we obtain the asymptotic properties including the asymptotic normality and the exact law of the iterated logarithm.