The conditional distance autocovariance function
Bibliographic record
Abstract
Abstract The partial autocorrelation function (PACF) is often used in time series analysis to identify the extent of the lag in an autoregressive model. However, the PACF is only suitable for detecting linear correlations. This article proposes the conditional distance autocovariance function (CDACF), which is zero if and only if measured time series components are conditionally independent. Due to the lack of this property, traditional tools for measuring partial correlations such as the PACF cannot work well for nonlinear sequences. Based on the CDACF, we introduce a tool known as an integrated conditional distance autocovariance function (ICDACF), which can test conditional temporal dependence structures of a sequence and estimate the order of an autoregressive process. Simulation studies reveal that the ICDACF can detect the conditional dependence of nonlinear autoregressive models efficiently while controlling for type‐I error rates. Finally, an analysis of a Bitcoin price dataset using the ICDACF demonstrates that our method has considerable advantages over other state‐of‐the‐art methods.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".