Edgeworth expansion for Bernoulli weighted mean

TL;DR

This paper derives an Edgeworth expansion for the Bernoulli weighted mean involving i.i.d. variables and Bernoulli weights, extending classical results to mixed semi-lattice and non semi-lattice cases for bootstrap consistency.

Contribution

It introduces a new Edgeworth expansion for Bernoulli weighted means with semi-lattice considerations, aiding bootstrap analysis in complex setups.

Findings

Provides the first Edgeworth expansion for Bernoulli weighted means with semi-lattice variables.

Defines semi-lattice distribution concept for higher-dimensional cases.

Facilitates bootstrap consistency proofs in online A/B testing scenarios.

Abstract

In this work, we derive an Edgeworth expansion for the Bernoulli weighted mean $\hat{\mu} = \frac{\sum_{i=1}^n Y_i T_i}{\sum_{i=1}^n T_i}$ 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 Cram\'er'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.