The efficiency of poststratification compared with model-assisted estimation
Bibliographic record
Abstract
Survey sampling with model-assisted estimation has recently gained popularity in forest inventory. Another option for utilizing the auxiliary information is to use poststratification, which is a special case of model-assisted estimation with class variables as explanatory variables. In this study, we compared the efficiency of poststratification with an increasing number of strata with model-assisted estimation. We carried out a study based on a simulated population. We considered four different types of poststratifications, namely (i) stratification based on predictions of a linear model, (ii) stratification based on a regression tree model, (iii) stratification based on the first principal component of the explanatory variables, and (iv) stratification based on the regression tree model with the first principal component as the only explanatory variable. Furthermore, we examined both the traditional poststratification mean and variance estimators and the difference estimator and its variance estimator for poststratification. Within the recommended range of number of strata, the model-assisted approach was more efficient than poststratification. With a large number of strata, poststratification produced smaller standard error of estimates, but problems such as empty strata were encountered with small sample sizes. Using the first principal component directly for stratification or as an explanatory variable was the most efficient approach.
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 | 0.027 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".