Minimum Hellinger distance estimation for a semiparametric location-shifted mixture model
Bibliographic record
Abstract
In this article, we propose a minimum Hellinger distance estimation (MHDE) for a semiparametric two-component mixture model where the two components are unknown location-shifted symmetric distributions f(x−μ1) and f(x−μ2). In the construction of MHDE, an appropriate estimation of the unknown nuisance parameter f is required. We propose to use the inversion formula given in Bordes et al. to estimate f based on current available sample from the mixture. To obtain the MHDE, an algorithm is presented to ease the numerical calculation. We also propose a simple but intuitive and robust initial estimator of the parameters. To assess its performance, we carry out a simulation study with comparison with a minimum profile Hellinger distance estimator (MPHDE) given in Wu et al. We use the proposed estimator to analyse the Old Faithful Geyser data in order to demonstrate its application. Through the numerical studies, we observe that our proposed MHDE for this semiparametric mixture model inherits the desired robustness and efficiency properties of that for parametric models. The proposed MHDE is very competitive with the MPHDE when there is no data contamination, whereas it performs better than the MPHDE in terms of bias when data is contaminated with outliers. Moreover, the MHDE reduces significantly the computing time of the MPHDE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".