Neutrosophic Statistics with Outliers Data: Utilizing Neutrosophic Median Absolute Deviation to Estimate the Mean Parameter Using Neutrosophic Modified One Step M-Estimator
Bibliographic record
Abstract
Classical statistical methods rely on precise data to estimate population means using auxiliary information but often face issues like bias and high mean squared error (MSE). Neutrosophic statistics extend classical approaches by incorporating vague, indeterminate, and uncertain data. This study introduces the Modified One-Step M-estimator (NMOM), which utilizes auxiliary information to improve estimation accuracy. The Neutrosophic Median Absolute Deviation (NMAD) is also employed to measure robustness against outliers and uncertainty. Empirical studies and simulations compare NMOM with the Neutrosophic Standard Mean (NSM) using metrics such as mean, median, standard deviation, covariance, NMAD, number of outliers, and NMSE. Results show that NMOM is more robust than NSM, particularly in managing outliers, reducing variance, and achieving lower MSE. The use of NMAD strengthens NMOM’s ability to produce reliable estimates under uncertain data conditions. This highlights NMOM’s effectiveness in fields like finance, engineering, and medicine, where data imprecision is a key concern.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".