## Influential observations in survival models with censored data: an approach based on the influence function |

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Accelerated Failure analyst approximation assessing the effect assume asymptotic influence function asymptotic variances baseline hazard function bioassays censored and uncensored Consider covariate vector Cox regression model defining function deleted derived differentiable distribution F distribution function dose level dose-response curve dose-response relationships effect of observations empirical influence function empirical influence values equation estimated survival probabilities evaluated exponential failure time distribution failure time models Failure Time regression failure-time functions for censored Furthermore given Hampel hazard function identification of influential independent random censorship Influence Diagnostics influential data influential observations information matrix ith observation wi joint distribution Kaplan-Meier estimator Knafl likelihood function linear logistic model location-scale regression model loss function maximum likelihood estimator models with censored Myeloma Patient Data observed failure observed information matrix p-vector parameter estimates parameter vector partial-likelihood prognostic factors quantal random censorship model random variables Robust Estimation sample scale invariant score function standard survivor function Theorem Uncensored Observation