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In this paper we deal with maximum likelihood estimation (MLE) of the parameters of a Pareto mixture. Standard MLE procedures are difficult to apply in this setup, because the distributions of the observations do not have common support. We study the properties of the estimators under different hypotheses; in particular, we show that, when all the parameters are unknown, the estimators can be found...
In recent years, adaptive Markov Chain Monte Carlo (MCMC) methods have become a standard tool for Bayesian parameter estimation. In adaptive MCMC, the past iterations are used to tune the proposal distribution of the algorithm. The same adaptation mechanisms can be used in Simulated Annealing (SA), a popular optimization method based on MCMC. The difficulty in using adaptation directly in SA is that...
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