An Advanced Statistical Method for Point Process Modelling With Missing Event Histories
Bibliographic record
Abstract
Researchers are always challenged in developing history-dependent point process models for recurrence events such as system failures when the early event history is missing. The raison d’être in most cases is the estimation of model parameters. Even for simple renewal point processes, the model estimation is difficult since the distribution of backward recurrence time is not explicitly given, except for the limiting distribution case. To fill the gap, this article establishes an effective statistical method for parameter estimation for history-dependent point process models with partial missing history. The proposed method addresses the missing history issue through a data augmentation (DA) technique integrated with a Markov Chain Monte Carlo (MCMC) simulation technique. The key novelty is the creation of an acceptance/rejection criterion to assure the validity of the augmented missing history. Next, the augmented and observed histories are combined to form a ‘complete’ history which is in turn used for model estimation. The estimated parameters are then used to generate new missing history. An iterative procedure is implemented to warrant stationary distributions for the estimates after burn-in. The validity and efficiency of the proposed method are demonstrated using two simulation studies and one real-life case study of modelling municipal water pipe failures from different model estimation perspectives.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".