Trend analysis of the power law process with censored data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper we assume that the failures of a system follow a non-homogenous Poisson process (NHPP) with a power law intensity function. NHPP is a model commonly used to describe a system with minimal repairs. In many situations, such as hidden failures, failure times of a system are subject to censoring. Current trend analysis methods in the literature for NHPP consider only right censoring and do not address recurrent failure data with left or interval censoring and periodic or non-periodic inspections. We use the likelihood ratio test to check for trend in the failure data. We use the EM algorithm and a recursive method to calculate the likelihood for estimating the parameters of the power law process in the case of null and alternative hypotheses (no trend and trend assumptions). As an example, the proposed method is applied to the failures of a medical infusion pump. It was found that the likelihood ratio test and the proposed recursive method can be applied successfully to censored data, although the method may be computationally intensive for larger datasets. We also compared the likelihood method to an ad-hoc method using the mid points of censoring intervals instead of unknown failure times. The comparison showed that using the midpoints is not reliable and may result in incorrect conclusion about the trend. The proposed method can be applied to other repairable systems used in industry.
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.
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 | 0.000 | 0.000 |
| 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