Semiparametric multi-parameter regression survival modelling
Research output: Contribution to journal › Journal article › Research › peer-review
Kevin Burke, Frank Eriksson, C. B. Pipper
We consider a log-linear model for survival data, where both the location and scale parameters depend on covariates and the baseline hazard function is completely unspecified. This model provides the flexibility needed to capture many interesting features of survival data at a relatively low cost in model complexity. Estimation procedures are developed and asymptotic properties of the resulting estimators are derived using empirical process theory. Finally, a resampling procedure is developed to estimate the limiting variances of the estimators. The finite sample properties of the estimators are investigated by way of a simulation study, and a practical application to lung cancer data is illustrated.
|Journal||Scandinavian Journal of Statistics|
|Number of pages||37|
|Publication status||Published - 2019|
- stat.ME, 62N01, 62N02, 62N03