… is to propose Bayesian case-deletion influence diagnostics for survival models. First, we
develop … The survival model we consider is the Cox model with a gamma process prior on the …

… model selection criteria are given. In order to examine outlying and influential observations,
we present a Bayesian case … Besides the proposed FGM bivariate survival model, we also …

… carry out Bayesian inference for log-BS-t regression models. Also, we introduce Bayesian
case influence diagnostics … A new class of survival regression models with heavy-tailed errors: …

… or semiparametric models has primarily focused on case influence diagnostic procedures, …
Proportional hazard model) We consider Bayesian analysis of the proportional hazards model …

… Survival was calculated in years from date of diagnosis to … -time and Bayesian spatial
approaches used in this case study. … and Bayesian spatial survival models used in this case study …

… Survival analysis takes a different approach, and models survival rate by taking into account
patient-… , to know the temporal order and duration of symptoms can influence the diagnostic …

… and diagnostics for model selection and model assessment … survival models based mainly
on the so-called hazard functions … to verify important modeling assumptions, identify influential …

… We specifically focus on a special hyper-Poisson distribution and then develop the
corresponding Bayesian simulation, influence diagnostics and an application to real dataset by …

… case deletion diagnostics for prediction of future observations in the log normal survival analysis
model… Our approach to prediction is Bayesian. Assuming censored log normal data and …

… diagnosis in one of the structures belonging to the Milano Cardiological Network, using a
logit model for the survival … Onset to Balloon time has a lighter influence on it. An interesting, …