Analysis of a Threshold Strategy in a Discrete-time Sparre Andersen Model
Bibliographic record
Abstract
In this thesis, it is shown that the application of a threshold on the \nsurplus level of a particular discrete-time delayed Sparre Andersen \ninsurance risk model results in a process that can be analyzed as a \ndoubly infinite Markov chain with finite blocks. Two fundamental \n cases, \nencompassing all possible values of the surplus level at the time of \n the first claim, are explored in detail. Matrix analytic methods are \n employed to establish a computational algorithm for each case. The \n resulting procedures are then used to calculate the probability \ndistributions associated with fundamental ruin-related quantities of \ninterest, such as the time of ruin, the surplus immediately prior to \nruin, and the deficit at ruin. The ordinary Sparre Andersen model, an \nimportant special case of the general model, with varying threshold \n levels is \nconsidered in a numerical illustration.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".