Markov chain Monte Carlo estimation of hazard model parameters in paleodemography
Bibliographic record
Abstract
Introduction In the early 1990s Konigsberg and Frankenberg wrote “A future direction that we expect to see in anthropological demography and paleodemography is the incorporation of uncertainty of age estimates into reduced parameterizations of life table functions. For example, hazards analysis, which reduces the mortality parameters to a small set, has recently been used in a number of anthropological demography studies” (Konigsberg and Frankenberg 1992:252). At the time we were writing we lacked the appropriate reference sample data for such an endeavor, as well as a number of the requisite statistical/computational tools. Today, neither of these issues is particularly problematical. Consequently, in this chapter we present some newer methods exploiting available reference sample data. The structure of this chapter is as follows. First, we discuss methods for modeling the dependence of an ordinal categorical variable on age. We then discuss the modeling of survivorship for archaeological human remains, and show how hazard model parameters can be estimated from an ordinal categorical variable using traditional maximization of the loglikelihood. We follow this presentation of methods with a brief example of estimating the parameters in a Gompertz–Makeham model using pubic symphyseal data and the method of maximum likelihood. We then turn to using a specific Markov chain Monte Carlo (MCMC) method known as the Gibbs Sampler to show how more general problems in hazard model and age estimation can be attacked.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".