Edgeworth Expansion for Bernoulli Weighted Mean
P.-L. Cauvin. Technical report, 2022.
In this work, we derive an Edgeworth expansion for the Bernoulli weighted mean \(\hat{\mu} = \frac{\sum_{i=1}^n Y_i T_i}{\sum_{i=1}^n}\) in the case where \(Y_1, \dots, Y_n\) are i.i.d. non semi-lattice random variables and \(T_1, \dots, T_n\) are Bernoulli distributed random variables with parameter \(p\). We also define the notion of a semi-lattice distribution, which gives a more geometrical equivalence to the classical Cramér’s condition in dimensions bigger than 1. Our result provides a first step into the generalization of classical Edgeworth expansion theorems for random vectors that contain both semi-lattice and non semi-lattice variables, in order to prove consistency of bootstrap methods in more realistic setups, for instance in the use case of online AB testing.