Laplace Distribution Based Robust Identification of Errors-in-Variables Systems With Outliers
Bibliographic record
Abstract
This article considers a robust identification problem of errors-in-variables (EIVs) system with nonlinear input generation process. In addition, we consider that the output data has both measurement noise and disturbance with outliers. The disturbance is described by a Laplace distribution to account for the outliers. Simultaneously, the input data is also contaminated by measurement noise. The parameters and posterior distributions of the proposed model are estimated by utilizing the expectation maximization algorithm as well as particle filter. Since the integrals of posterior state estimation cannot be derived analytically due to nonlinearity, a bank of weighted particles is utilized to approximate the states. The efficacy of the proposed model is demonstrated by simulation and experimental studies. Finally, the proposed robust modeling method is compared with three other robust modeling methods through comparison of mean absolute error (MAE) and mean relative error (MRE) values. The results demonstrate that the proposed method achieves smaller MAE and MRE, indicating its superior robustness in handling both noisy input–output data and outlier-corrupted outputs.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".