Comparison of Two Means of Two Log-Normal Distributions When Data is Singly Censored
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Bibliographic record
Abstract
It is common in environmental and biomedical data analysis to dealwith censored data that are log-normally distributed. This paperis concerned with the statistical analysis for comparing the meansof two independent log-normal distributions from censored datawith a single detection limit. The method of maximum likelihoodwill be used to obtain closed form estimates for populationparameters under different hypotheses. A test procedure forcomparing the means of two independent log-normal populations inthe presence of censored data is also introduced and evaluated.Asymptotic chi-square test is used in the proposed test procedure.Worked example is given illustrating the use of the methodsprovided utilizing a computer program written in the R language.A simulation study was performed to examine the power of the proposed test procedure introduced in this article.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it