Proposed Distance-Based Test for Testing Multivariate Multiple Regression Coefficients under Restricted Alternatives
Why this work is in the frame
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Bibliographic record
Abstract
In constructing estimation and hypothesis testing procedures, it is important that all available information such as sign of parameter is used in order to maximize power of the test. Often prior information are known about the sign of regression coefficients (parameter) under test, the best example being that variances cannot be negative. Ignoring information about the signs of regression parameters can lead to loss of power in small samples. With this problem in mind, this paper concerned with developing restricted estimation and hypothesis testing approach in the context of multivariate multiple regression model. Developing the technique of estimating constraint regression coefficients and testing restricted parameters with the aid of information theoretic distance are the main contribution of this paper. The distribution of the existing two-sided test follows central chi-square distribution whereas the test statistic of our proposed distance-based one-sided test follows weighted mixture of chi-square distribution. Monte Carlo simulation indicates that our newly proposed test performs better than existing tests.
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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 | 0.028 |
| 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 | 0.000 | 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