Bibliographic record
Abstract
This thesis explores different techniques for quantifying risk. Risk is a concept that permeates several branches of mathematics as well as fields for which statistical analysis is crucial. Examples include economics, actuarial sciences, finance, and hydrology. In particular, risk is examined here through the use of risk measures and statistical modeling.First, we explore the estimation of multivariate risk measures. Specifically, we develop a semi-parametric estimation procedure for expectiles for extreme levels of risk. Multivariate expectiles and their extremes have been the focus of plentiful research recently, including estimation in extreme scenarios. However, current estimation techniques in an extreme value framework are restricted to the limiting cases of upper tail dependence: independence and comonotonicity. In this thesis, an alternative optimization problem along with a consistent estimation scheme is presented, which can solve for multivariate extreme expectiles without having to assume any underlying dependence structure. Specifically, we show that if the upper tail dependence function, tail index, and tail ratio can be consistently estimated, then one would be able to accurately estimate multivariate extreme expectiles. The finite-sample performance of this methodology is demonstrated using both simulated and real data.Second, we build a hierarchical Bayesian model for quantifying the prevalence and magnitude of extreme surges on the Atlantic Coast of Canada with limited data. Generalized extreme value distributions are fitted marginally to surge observations at 21 buoys within our domain, and the modeling hierarchy includes latent Gaussian fields whose means and variances are driven by the atmospheric sea-level pressure and the distance between the buoys, respectively.Incorporating this spatial information allows the model to share information between buoys and, more importantly, allows for spatial interpolation to be conducted at locations with no observations. Additionally, introducing a copula into the hierarchy allows for a continuous representation of extreme surges, which extends the inferential capabilities of the models beyond a site-by-site basis. Using realization of extreme surges simulated from the model and combining it with the physically driven tidal process at each location, we are able to predict potentially catastrophic water levels
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 | 0.021 |
| 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 | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".