Modelos de regressão quando a função de taxa de falha não é monótona e o modelo probabilístico beta Weibull modificada
Bibliographic record
Abstract
Regression models when the failure rate function is no monotone and the new beta modified Weibull modelIn survival analysis applications, the failure rate function may have frequently unimodal or bathtub shape, that is, non-monotone functions.The regression models commonly used for survival studies are log-Weibull, monotone failure rate function shape, and log-logistic, decreased or unimodal failure rate function shape.In the first part of this thesis, we propose location-scale regression models based on an extended Weibull distribution for modeling data with bathtub-shaped failure rate function and on a Burr XII distribution as an alternative to the log-logistic regression model.Assuming censored data, we consider a classical analysis, a Bayesian analysis and a jackknife estimator for the parameters of the proposed models.For these models, we derived the appropriate matrices for assessing the local influence on the parameter estimates under different perturbation schemes, and we also presented some ways to perform global influence.Additionally, we developed residual analysis based on the martingale-type residual.For different parameter settings, sample sizes and censoring percentages, various simulation studies were performed and the empirical distribution of the martingale-type residual was displayed and compared with the standard normal distribution.These studies suggest that the empirical distribution of the martingale-type residual for the log-extended Weibull regression model with data censured present a high agreement with the standard normal distribution when compared with other residuals considered in these studies.For the log-Burr XII regression model, it was proposed a change in the martingale-type residual based on some studies of simulation in order to obtain an agreement with the standard normal distribution.Some applications to real data illustrate the usefulness of the methodology developed.It can also happen in some applications that the assumption of independence of the times of survival is not valid, so it was added to the log-Burr XII regression model of random effects for which an estimate method was proposed for the parameters based on the EM algorithm for Monte Carlo simulation.Finally, a fiveparameter distribution so called the beta modified Weibull distribution is defined and studied.The advantage of that new distribution is its flexibility in accommodating several forms of the failure rate function, for instance, bathtub-shaped and unimodal shape, and it is also suitable for testing goodness-of-fit of some special sub-models.The method of maximum likelihood is used for estimating the model parameters.We calculate the observed information matrix.A real data set is used to illustrate the application of the new distribution.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".