July 20, 2024Statistica Neerlandica

A note on convergence of calibration weights to inverse probability weights

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Calibration weights converge asymptotically to inverse probability weights under specified theoretical conditions, linking two distinct approaches for survey nonresponse.
Theoretical model establishes mathematical equivalence using auxiliary variables observed in respondents, while incorporating known population distributions of survey items.
Highlights clarify structural connections between calibration and propensity score methods, which may support more robust adjustments for nonresponse bias in sample surveys.

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Implication

Theoretical analysis demonstrates convergence of calibration weights to inverse probability weights in sample surveys, highlighting shared mechanisms for nonresponse bias adjustment.

Cite This Study

Tadayoshi Fushiki (2024) studied this question.

synapsesocial.com/papers/68e5fa66b6db64358758e9c4 https://doi.org/10.1111/stan.12356

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Implication

Theoretical analysis demonstrates convergence of calibration weights to inverse probability weights in sample surveys, highlighting shared mechanisms for nonresponse bias adjustment.

A note on convergence of calibration weights to inverse probability weights

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Implication

Cite This Study

A note on convergence of calibration weights to inverse probability weights

Key Points

Discussion

Implication

Cite This Study

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