Print  Close

Bootstrap Inference with Stacked Multiple Imputations

Abstract Number:

3606 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Paul Bernhardt (1)

Institutions:

(1) Villanova University, N/A

First Author:

Paul Bernhardt  
Villanova University

Presenting Author:

Paul Bernhardt  
Villanova University

Abstract Text:

Stacked multiple imputation for missing data modifies the usual multiple imputation approach by stacking the m imputed data sets into a single data set for analysis. Various advantages of the stacked approach have been previously demonstrated (e.g., Beesley and Taylor, 2020 & 2021). We explore bootstrap approaches with stacked multiple imputation, similar to those suggested by Schomaker and Heumann (2018) for usual multiple imputation. We demonstrate that bootstrap inference with stacked multiple imputations has modest advantages in some settings with respect to computation and estimation.

Keywords:

Multiple Imputation|Stacked Multiple Imputation|Bootstrap| Missing Data| |

Sponsors:

Biometrics Section

Tracks:

Missing Data

Can this be considered for alternate subtype?

Yes

Are you interested in volunteering to serve as a session chair?

Yes

I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.

I understand