A Selection Model for Bivariate Normal Data, with a Flexible Nonparametric Missing Model and a Focus on Variance Estimates

Nonignorable nonresponse is a common problem in bivariate or
multivariate data. Here a selection model for bivariate normal
distributed data (Y1 ; Y2) is proposed. The missingness of Y2 is
supposed to depend on its own values. The model for missingness
describes the probability of nonresponse in dependency of Y2 itself
and it is chosen nonparametrically to allow exible patterns. We try
to get a reasonable estimate for the expectation and especially for
the variance of Y2 . Estimation is done by data augmentation and
computation by common sampling methods.