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EN
The concept of robustness of statistical procedures is one of the most important subject in Zielinski's papers. In this article the development of the idea of robustness as introduced in Zielinski's papers is presented. The definitions of a supermodel and a robustness function are given. The problem of the robust estimation of a scale parameter in an exponential model and the robustness of tests for comparison of means in two or more populations are described. Robustness in Bayesian statistical models is connected with an unexactly specified prior distribution. Here the following Zielinski's results in Bayesian robustness are presented: the most stable estimator in the Poisson model and the Bayes optimal stopping rule in a homogeneous Poisson process with conjugate classes of priors, the optimal experimental designs in Bayesian linear models under variation in the prior, an upper bound for the Kolmogorov distance between the posterior distributions in terms of that between the prior distributions.
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