Starting values for the iterative maximum likelihood estimator in survival analysis

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Abstract
Maximum likelihood estimation of parametric or semi-parametric proportional hazard models requires an iterative procedure, since closed-form solutions are difficult to come by, because of non-linearities. Here, I propose an approximate maximum Program packages such as GAUSS and SAS typically use the ordinary lease-squares so the starting values can yield slow convergence and even a local rather than a global maximum solution. The AML estimates, however, are excellent starting values and are just as easily calculated.

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