Fisher information contained in incomplete observations

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Informative censoring can maintain Fisher information, leading to better statistical estimates of parameters.
The study identifies that not all censoring results in information loss, contrary to common assumptions.
Assessment of parameter estimation quality was based on Cramer-Rao efficiency and Fisher information criteria in models of incomplete observations and censoring effects.  Comparison with standard methods highlights the importance of considering data acquisition techniques when analyzing incomplete data.

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Randomized analysis shows that informative censoring preserves Fisher information in statistical models, implying better outcomes.

Abdushukurov et al. (2024) studied this question.

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