Irregularly Spaced Time Series Data with Time Scale Measurement Error
Bibliographic record
Abstract
This project can be mainly divided into two sections.In the first section it attempts to model an irregularly spaced time series data where time scale is being measured with a measurement error.Modelling an irregularly spaced time series data alone is quite challenging as traditional time series techniques only capture equally/regularly spaced time series data.In addition to that, the measurement error in the time scale make it even more challenging to incorporate measurement error models and functional approaches to model the time series.Thus, this project is based on a Bayesian approach to model a flexible regression function when the time scale is being measured with a measurement error.The regression functions are modelled with regression P-splines and the exploration of posterior is carried out using a fully Bayesian method that uses Markov chain monte carlo (MCMC) techniques.In section two, we identify the relationship/dependency between two irregularly spaced time series data sets which were modelled using regression P-splines and a fully Bayesian method, using windowed moving correlations.The validity of the suggested methodology is then explored using two simulations.It is then applied on two irregularly spaced time series data sets each subjected to measurement errors in time scale to identify the dependency between them in terms of statistically significant correlations.First and foremost, I would like to express my gratitude to my supervisor Dr.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".