Estimation of Right-censored SETAR-type Nonlinear Time-series Model
Bibliographic record
Abstract
This paper focuses on estimating the Self-Exciting Threshold Autoregressive (SETAR) type time-series model under right-censored data. As is known, the SETAR model is used when the underlying function of the relation-ship between the time-series itself ( Y t ), and its p delays $$({Y_{t - j}})_{j = 1}^p$$ violates the lin-earity assumption and this function is formed by multiple behaviors that called regime. This paper addresses the right-censored dependent time-series problem which has a serious negative effect on the estimation performance. Right-censored time series cause biased coefficient estimates and unqualified predictions. The main contribution of this paper is solving the censorship problem for the SETAR by three different techniques that are kNN imputation which represents the imputation techniques, Kaplan-Meier weights that is applied based on the weighted least squares, synthetic data transformation which adds the effect of censorship to the modeling process by manipulating dataset. Then, these solutions are combined by the SETAR-type model estimation process. To observe the behavior of the nonlinear estimators in practice, a simulation study and a real data example are carried out. The Covid-19 dataset collected in China is used as real data. Results prove that although the three estimators show satisfying performance, the quality of the estimate SETAR model based on the kNN imputation technique dominates the other two estimators.
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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 | 0.002 |
| 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".