Basic Assumptions for the MI Method

The MI method is provided under the following assumptions:

1. The base model for the Summary Statistics version of MI is a multivariate normal distribution with parameters (μ, Σ)
   where

   1. μ is a vector of means.

   2. Σ is a variance-covariance matrix.

2. Prior distribution of μ is a conditionally-multivariate Gaussian given Σ with parameters μ₀∈R⁷ and τ^-1Σ, where τ is a positive constant. The variance-covariance matrix Σ follows the inverted-Wishart distribution for fixed parameters m ≥ p and a positive-definite matrix Λ.

3. Data points are Missed At Random (MAR).

The strict definition of this and other mechanisms supporting missing values are available in [Rubin1987].