Identification and Efficient Estimation in Regression Analysis with Response Missing Not At Random
Why this work is in the frame
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Bibliographic record
Abstract
Missing-data is a pervasive problem in regression analysis, compromising the accuracy and efficiency of parameter estimates.This paper focuses on the challenging scenario of missing not at random (MNAR) data, where the missingness of a value is linked to the value itself.Traditional approaches to addressing MNAR data confront a trade-off: imposing stringent assumptions about the missingness mechanism can enhance efficiency but curtail robustness, whereas accommodating model misspecification can bolster robustness but at the expense of efficiency.In addition, assuming a nonparametric MNAR mechanism will lead to model identifiability issues.We propose a novel approach that overcomes this limitation.Firstly, we address the model identifiability issue using the shadow variable.Then, by leveraging the sieve method, we can model the MNAR mechanism nonparametrically.This approach achieves the best of both worlds: it gains robustness by avoiding strict assumptions about the missingness mechanism while simultaneously achieving the semiparametric efficiency bound for the parameter of interest (meaning our estimator has the lowest possible Statistica Sinica: Newly accepted Paper asymptotic variance).The paper delves into the theoretical framework, outlining conditions for identifiability, constructing the semiparametric likelihood function, and rigorously proving the estimator's semiparametric efficiency.Additionally, we present an EM-type algorithm for practical implementation, discussing the E-step and M-step iterations and variance estimation methods.Finally, simulations and a real-data application demonstrate the effectiveness of our proposed method compared to existing approaches.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it