Semiparametric Estimation for Error-Prone Partially Linear Single-Index Models
Why this work is in the frame
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Bibliographic record
Abstract
Partially linear single-index models prove to be flexible in facilitating various types of relationships between the outcome and covariates. However, their validity is hampered by the presence of measurement error in covariates, a feature commonly encountered in applications. In this paper, we explore the use of such models to handle data subject to measurement error in both parametric and nonparametric terms. In addition, with multivariate covariates, often a few of them are informative while most of them are not. In this paper, we propose the three stage procedure to eliminate measurement error effects and select important variables for both the linear predictor term and the single-index part. To implement the proposed method efficiently, we develop a boosting algorithm that enables us to select variables and estimate the parameters without handling non-differentiable penalty functions. Theoretical results, including consistency and asymptotic normality of the estimator, are established to justify the validity of the proposed method. In addition, we examine statistical properties of the boosting algorithm, including convergence and validity of variable selection. Numerical studies, including simulation and data analysis, are conducted to assess the finite sample performance of the proposed method.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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