Simultaneous Hypothesis Testing of Multivariable Nonparametric Spline Regression in the GWR Model
Bibliographic record
Abstract
In this research, studied multivariable nonparametric geographically weighted regression use truncated spline approach. The model is an expansion of nonparametric truncated spline regression that takes into account geographical or spatial factors. The purpose of this study was to find statistics test and distribution for the simultaneous hypothesis test. This study obtains the statistic test used the maximum likelihood ratio test (MLRT) method. Results of the research obtained statistics test based on the ratio between the maximum of the likelihood function under the set of H_0 and the maximum of the set likelihood function below the population with each have a spatial factor. Distribution of statistical tests has been proven to have a distribution of F. The modeling application used the percentage of the death of Dengue Hemorrhagic Fever (DHF) in 38 districts/cities in East Java Province. The modeling resulted in the determination coefficient of 80.7% and SSE value that is 0.0043.
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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 | 0.033 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".