GOODNESS-OF-FIT TESTS OF A PARAMETRIC DENSITY FUNCTIONS: MONTE CARLO SIMULATION STUDIES
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Bibliographic record
Abstract
The purpose of this paper is to use Monte Carlo simulations to evaluate the performance of six most popular statistics for testing the goodness of fit of a parametric density function. The first three tests in this study are based on the empirical distribution function which are simple and widely used. The other three are based on directed and non-directional divergence measures and derived from minimum relative entropy (MinxEnt) principle, m-spacing method and kernel method. This study aims to evaluate the behavior of these tests by examining the rejection rates under the hypothesis. It is shown that the tests based on the directed divergence measure give a good approximation to the given significance levels and are more powerful than other tests against the given alternative distributions. It also suggests that the statistics based on the MinxEnt estimator detect the distribution with higher kurtosis better than others.
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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 | 0.004 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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