Due to recent theoretical findings and advances in statistical computing, there has been a
rapid development of techniques and applications in the area of missing data analysis …

We consider identification and estimation with an outcome missing not at random (MNAR).
We study an identification strategy based on a so-called shadow variable. A shadow …

In database of recommender systems, users' ratings for most items are usually missing,
resulting in selection bias when users selectively choose items to rate. To address this …

Suppose we are interested in the mean of an outcome variable missing not at random.
Suppose however that one has available a fully observed shadow variable, which is …

We consider identification and inference about mean functionals of observed covariates and
an outcome variable subject to non-ignorable missingness. By leveraging a shadow …

Missing data occur frequently in empirical studies in health and social sciences, often
compromising our ability to make accurate inferences. An outcome is said to be missing not …

We consider the estimation problem in a regression setting where the outcome variable is
subject to nonignorable missingness and identifiability is ensured by the shadow variable …

We study the problem of matrix completion when the missingness of the matrix entries is
dependent on the unobserved response values themselves and hence the missingness …

Medical imaging has become an increasingly important tool in screening, diagnosis,
prognosis, and treatment of various diseases given its information visualization and …

Nonresponse is frequently encountered in empirical studies. When the response
mechanism is missing not at random (MNAR) statistical inference using the observed data is …