Abstract:
:Cox proportional hazard regression model is a popular tool to analyze the relationship between a censored lifetime variable with other relevant factors. The semiparametric Cox model is widely used to study different types of data arising from applied disciplines such as medical science, biology, and reliability studies. A fully parametric version of the Cox regression model, if properly specified, can yield more efficient parameter estimates, leading to better insight generation. However, the existing maximum likelihood approach of generating inference under the fully parametric proportional hazards model is highly nonrobust against data contamination (often manifested through outliers), which restricts its practical usage. In this paper, we develop a robust estimation procedure for the parametric proportional hazards model based on the minimum density power divergence approach. The proposed minimum density power divergence estimator is seen to produce highly robust estimates under data contamination with only a slight loss in efficiency under pure data. Further, it is always seen to generate more precise inference than the likelihood based estimates under the semiparametric Cox models or their existing robust versions. We also justify their robustness theoretically through the influence function analysis. The practical applicability and usefulness of the proposal are illustrated through simulations and real data examples.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Ghosh A,Basu Adoi
10.1002/sim.8377subject
Has Abstractpub_date
2019-11-30 00:00:00pages
5283-5299issue
27eissn
0277-6715issn
1097-0258journal_volume
38pub_type
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