
Frailty model examines the effect of observable and non-observable factors on time to event data. Presence of collinearity produces unstable estimates of parameters. Therefore, this research focus on the penalized estimation of frailty model and proposed the new estimator which is the extension of ridge and principal component estimators. Simulation is run to reveal the performance of proposed estimator. Moreover, the technique is applied on NFHS (National Family Health Survey) data to examine the infant mortality in India.
Frailty model, Social sciences (General), H1-99, Collinearity, Q1-390, Science (General), Ridge estimator, Principal component estimator, Infant mortality, Research Article
Frailty model, Social sciences (General), H1-99, Collinearity, Q1-390, Science (General), Ridge estimator, Principal component estimator, Infant mortality, Research Article
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
