A Data-Driven Algorithm for Parameter Estimation in the Parametric Survival Mixture Model

Abstract

<p> We propose a data-driven estimation algorithm in survival mixture model. The objective of this study is to provide an alternative fitting procedure to the conventional EM algorithm. The EM algorithm is the classical ML fitting of the parametric mixture model. If the initial values for the EM algorithm are not properly chosen, the maximizers might be local or divergent. Traditionally, initial values are given manually according to experience or a gridpoint search. This is a heavy burden for a high-dimensional data sets. Also, specifying the ranges of parameters for a grid-point search is difficult. To avoid the specification of initial values, we employ the random partition. Then, improvement of fitting is adjusted according to model specification. This process is repeated a large number of times, so it is computer intensive. The large repetitions makes the solution more likely to be the global maximizer, and it is driven purely by the data. We conduct a simulation study for three cases of two-component Log-Normal, two-component Weibull, and two-component Log-Normal and Wei bull, in order to illustrate the effectiveness of the proposed algorithm. Finally, we apply our algorithm to a breast cancer study data which follows a cure model. The program is written in R. It calls existing R functions, so it is flexible to use in regression situations where model formula must be specified. </p>