scholarly journals Correction for “Doubly robust estimation, optimally truncated inverse-intensity weighting and increment-based methods for the analysis of irregularly observed longitudinal data” by Eleanor Pullenayegum and Brian Feldman

2013 ◽  
Vol 33 (3) ◽  
pp. 540-540
Author(s):  
Eleanor M. Pullenayegum
Keyword(s):  
Longitudinal Data ◽  
Robust Estimation ◽  
Doubly Robust Estimation ◽  
Doubly Robust

scholarly journals Adjustment for Missing Data in Complex Surveys Using Doubly Robust Estimation

Epidemiology ◽  
2010 ◽  
Vol 21 (6) ◽  
pp. 863-871 ◽  
Author(s):  
Kathleen E. Wirth ◽  
Eric J. Tchetgen Tchetgen ◽  
Megan Murray
Keyword(s):  
Missing Data ◽  
Robust Estimation ◽  
Complex Surveys ◽  
Doubly Robust Estimation ◽  
Doubly Robust

Comparing Alternatives for Estimation from Nonprobability Samples

2019 ◽  
Vol 8 (2) ◽  
pp. 231-263 ◽  
Author(s):  
Richard Valliant
Keyword(s):  
Robust Estimation ◽  
Population Control ◽  
Simulation Studies ◽  
Data Set ◽  
External Data ◽  
Doubly Robust Estimation ◽  
Doubly Robust ◽  
External Population ◽  
Selection Probabilities ◽  
As If

Abstract Three approaches to estimation from nonprobability samples are quasi-randomization, superpopulation modeling, and doubly robust estimation. In the first, the sample is treated as if it were obtained via a probability mechanism, but unlike in probability sampling, that mechanism is unknown. Pseudo selection probabilities of being in the sample are estimated by using the sample in combination with some external data set that covers the desired population. In the superpopulation approach, observed values of analysis variables are treated as if they had been generated by some model. The model is estimated from the sample and, along with external population control data, is used to project the sample to the population. The specific techniques are the same or similar to ones commonly employed for estimation from probability samples and include binary regression, regression trees, and calibration. When quasi-randomization and superpopulation modeling are combined, this is referred to as doubly robust estimation. This article reviews some of the estimation options and compares them in a series of simulation studies.

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