First-order random coefficient autoregressive (RCA(1)) model: Joint Whittle estimation and information
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Bibliographic record
Abstract
Random coefficient autoregressive model, RCA(p), has been discussed widely as a suitable model for nonlinear time series. The conditional least squares and likelihood parameter estimation of RCA(p) model has also been discussed in [3]. The statistical inference of RCA(1) model has been presented in [4] while the conditional least square estimates for nonstationary processes is studied in [7]. The optimal estimation for nonlinear time series using estimating equations has been investigated in [6]. Recently there has been interest in joint prediction based on spectral density of popular nonlinear time series models such as RCA(p) models. Another way of estimating the parameters of the RCA(1) model is to do Whittle's estimation. In this paper the Whittle estimates of the parameters of an RCA(p) model are studied. It is shown that the Whittle information of the autoregressive parameter in an RCA(p) model is larger than the corresponding information in an autoregressive (AR) model.
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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 | 0.001 |
| 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 | 0.002 |
| 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