Maximum likelihood estimates, AIC (best value in bold).[]{data-label="table:NerveMLE"}

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   (a)   (b)
        
   (c)   (d)
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Example 2: PBC data
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In this section, we analyse the popular Mayo primary biliary cirrhosis (PBC) data, reported in Appendix D from [@FH91], in order to illustrate the performance of the proposed distributions in the context of AFT models. This data set contains information about the survival time and prognostic factors for 418 patients in a study conducted at Mayo Clinic between 1974 and 1984. The survival times are reported in days together with an indicator variable associated to the status of the patient at the end of the study (0/1/2 for censored, transplant, dead). [@J03] fitted, using a semiparametric method, an AFT model with five covariates: age (in years), logarithm of the serum albumin (in mg/dl), logarithm of the serum bilirubin (in mg/dl), edema, and logarithm of the prothrombin time (in seconds). Similarly, [@D10] reports the semiparametric estimators of the AFT model with an intercept parameter as follows: $(8.692,-0.025,1.498,-0.554,-0.904,-2.822)$. We consider a maximum likelihood estimation approach of the AFT model (\[AFTModel\]) containing an intercept and LTP $t$ errors with parameters $(0, \sigma_{\varepsilon}, \gamma_{\varepsilon}, \delta_{\varepsilon})$. The estimators and the AIC values are reported 