Variable selection in proportional hazards cure model with time-varying covariates, application to bank failures
Bibliographic record
Abstract
In the last three decades, as a consequence of failures and corporate actions, the number of commercial banks in the United States has shrunk by two thirds. Empirical evidence in the analysis of bank failures suggests the existence of banks which are not susceptible to default. For this reason, we use a semi-parametric proportional hazards cure model with time-varying covariates to study their effects either on the probability that a bank is susceptible to default and on the survival time of failed institutions. We propose a penalized maximum likelihood method for the selection of the most significant variables, using a Smoothly Clipped Absolute Deviation (SCAD) penalty. A simulation study shows that this procedure performs reasonably well. We apply this methodology to a quite large sample of United States commercial banks insured by the Federal Deposit Insurance Corporation (FDIC) and with more than 50 million dollars of total assets during the last quarter of 2002. More in detail, we use bank-specific covariates observed on a quarterly basis until the end of 2015, that we use as proxies for capital adequacy, asset quality, earnings, management efficiency, liquidity, cost structure and size.
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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".