Generalized Conversion Formulas between Multiple Decrement Models and Associated Single Decrement Models
Bibliographic record
Abstract
다중탈퇴모형 연구에서 연(year) 기준의 다중탈퇴율과 연 기준의 절대탈퇴율을 상호 전환하는 방법에 집중되어 있다. 실제 실무에서는 월(month) 기준의 다중탈퇴율이 필요한 경우가 많으므로 본 논문에서는 연 기준의 절대탈퇴율을 월 기준의 다중탈퇴율로 전환하거나 연 기준의 다중탈퇴율을 일 기준의 절대탈퇴율로 전환하는 공식을 유도한다. 유도된 공식은 월 기준 대신에 일(day) 기준 또는 분기(quarter) 기준 또는 반기(semiannual) 기준 등으로도 전환 가능한 공식이다. 또한 월 기준의 절대탈퇴율에서 월 기준의 다중탈퇴율로 전환 가능한 공식도 제시한다. 절대탈퇴율에서 다중탈퇴율로 전환하는 과정에서 절대탈퇴율이 균등분포 가정(UDD: Uniform Distribution of Decrements)을 따른다고 한다. 다중탈퇴율에서 절대탈퇴율로 전환하는 과정에서는 다중탈퇴율이 UDD를 가정하는 경우와 상수탈퇴력 가정 (Constant force assumption)을 따르는 경우로 나누어서 공식을 유도한다. 유도된 공식은 Bowers 등 (1997)에 있는 전환 공식의 일반적인 형태임을 확인할 수 있다. 또한 유도된 공식을 활용하여 수치 예를 통하여 자료를 이용하여 절대탈퇴율과 다중탈퇴율의 전환 과정을 설명하며 유도된 공식들의 차이점을 비교한다. Researches on conversion formulas between multiple decrement models and the associated single decrement models have focused on calculating yearly-based conversion formulas. In practice, actuaries may be more interested in monthly-based conversion formulas. Multiple decrement tables and their associated single decrement tables consist of yearly-based rates of multiple decrements and absolute rates of decrements, respectively. This paper derives conversion formulas from yearly-based absolute rates of decrements to monthly-based rates of decrement due to cause j under the uniform distribution of decrements(UDD). Next, it suggests conversion formulas from monthly-based absolute rates of decrements to monthly-based rates of decrement due to cause j under UDD. In addition, it calculates conversion formulas from yearly-based rates of decrement due to cause j to the corresponding absolute rates of decrements under UDD or constant force assumption. Some numerical examples are discussed.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".