Optimized Dickey-Fuller Test Refines Sign and Boundary Problems Compare to Traditional Dickey-Fuller Test
Bibliographic record
Abstract
Impede nonstationarity is vigorous to study performance of time series data and removes long-term components to expose any regular short-term regularity. So, we find miscellaneous unit root tests for instance Dickey-Fuller test, Augmented Dickey-Fuller plus DF-GLS Tests and identify that almost all unit root tests with the estimated model suffer from sign and boundary problems of the parameters to smooth the progress of the non-stationarity problem. In this paper, we usage Dickey-Fuller test and impose some limits on the parameter. Our proposed optimized DF test based on error sum of square (ESS). Monto Carlo simulation method is used to generate simulated critical values for different sample size. Our proposed optimized DF test gives better result than the ordinary DF test with effectiveness, uniformity and power properties. Also, optimized DF improves the sign and boundary problems through imposing some limit on error sum of squares and capture more nonstationarity of time related data.
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".