Pages that link to "Item:Q297131"

The following pages link to Destructive negative binomial cure rate model and EM-based likelihood inference under Weibull lifetime (Q297131):

Displaying 15 items.

• An EM algorithm for the destructive COM-Poisson regression cure rate model (Q682051) (← links)
• A stochastic version of the EM algorithm for mixture cure model with exponentiated Weibull family of lifetimes (Q2156017) (← links)
• Likelihood inference for the destructive exponentially weighted Poisson cure rate model with Weibull lifetime and an application to melanoma data (Q2358928) (← links)
• Two alternative estimation procedures for the negative binomial cure rate model with a latent activation scheme (Q2814280) (← links)
• A new non-linear conjugate gradient algorithm for destructive cure rate model and a simulation study: illustration with negative binomial competing risks (Q5055175) (← links)
• Likelihood inference based on EM algorithm for the destructive length-biased Poisson cure rate model with Weibull lifetime (Q5084729) (← links)
• On some inferential issues for the destructive COM-Poisson-generalized gamma regression cure rate model (Q5087982) (← links)
• A semiparametric promotion time cure model with support vector machine (Q6138594) (← links)
• On the estimation of destructive cure rate model: A new study with exponentially weighted Poisson competing risks (Q6149015) (← links)
• On the estimation of interval censored destructive negative binomial cure model (Q6560508) (← links)
• Enhancing cure rate analysis through integration of machine learning models: a comparative study (Q6581679) (← links)
• On the parameter estimation of Box-Cox transformation cure model (Q6625797) (← links)
• On the integration of decision trees with mixture cure model (Q6626940) (← links)
• A simplified stochastic EM algorithm for cure rate model with negative binomial competing risks: an application to breast cancer data (Q6628183) (← links)
• On a reparameterization of a flexible family of cure models (Q6628536) (← links)