Identification and semiparametric efficiency theory of nonignorable missing data with a shadow variable
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 …
We study an identification strategy based on a so-called shadow variable. A shadow …
Uncovering the propensity identification problem in debiased recommendations
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 …
resulting in selection bias when users selectively choose items to rate. To address this …
Identification, doubly robust estimation, and semiparametric efficiency theory of nonignorable missing data with a shadow variable
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 …
We study an identification strategy based on a so-called shadow variable. A shadow …
Using proxy pattern-mixture models to explain bias in estimates of COVID-19 vaccine uptake from two large surveys
RR Andridge - Journal of the Royal Statistical Society Series A …, 2024 - academic.oup.com
Recently, attention was drawn to the failure of two very large internet-based probability
surveys to correctly estimate COVID-19 vaccine uptake in the US in early 2021. Both the …
surveys to correctly estimate COVID-19 vaccine uptake in the US in early 2021. Both the …
Testing the missing at random assumption in generalized linear models in the presence of instrumental variables
Practical problems with missing data are common, and many methods have been
developed concerning the validity and/or efficiency of statistical procedures. On a central …
developed concerning the validity and/or efficiency of statistical procedures. On a central …
Semiparametric estimation in generalized additive partial linear models with nonignorable nonresponse data
J Du, X Cui - Statistical Papers, 2024 - Springer
We address the semiparametric challenge of identifying and estimating generalized additive
partial linear models with nonignorable missingness in the response. Identifiability is …
partial linear models with nonignorable missingness in the response. Identifiability is …
Sufficient identification conditions and semiparametric estimation under missing not at random mechanisms
Conducting valid statistical analyses is challenging in the presence of missing-not-at-
random (MNAR) data, where the missingness mechanism is dependent on the missing …
random (MNAR) data, where the missingness mechanism is dependent on the missing …
DNA-SE: towards deep neural-nets assisted semiparametric estimation
Semiparametric statistics play a pivotal role in a wide range of domains, including but not
limited to missing data, causal inference, and transfer learning, to name a few. In many …
limited to missing data, causal inference, and transfer learning, to name a few. In many …
A unified framework of analyzing missing data and variable selection using regularized likelihood
Y Bian, YY Grace, W He - Computational Statistics & Data Analysis, 2024 - Elsevier
Missing data arise commonly in applications, and research on this topic has received
extensive attention in the past few decades. Various inference methods have been …
extensive attention in the past few decades. Various inference methods have been …
Doubly Flexible Estimation under Label Shift
In studies ranging from clinical medicine to policy research, complete data are usually
available from a population P, but the quantity of interest is often sought for a related but …
available from a population P, but the quantity of interest is often sought for a related but …