A0464
Title: Tests of missing completely at random based on sample covariance matrices
Authors: Alberto Bordino - University of Warwick (United Kingdom)
Thomas Berrett - University of Warwick (United Kingdom) [presenting]
Abstract: One of the most commonly encountered discrepancies between real data sets and models hypothesized in theoretical work is that of missing data. When faced with incomplete data, the primary concern is to understand the relationship between the data-generating and missingness mechanisms. In the ideal situation, these two sources of randomness are independent, a setting known as missing completely at random (MCAR), but this is often too restrictive in practice. Tests of the MCAR hypothesis are considered, drawing connections with the matrix completion literature and thus developing tests based on semidefinite programming. Methods are more widely applicable than existing methods, and in cases where existing methods are applicable, strong empirical performance with comparable power is seen.
