Robust Statistics

Wan Tang & Qin Yu & Xin Tu

Maximum likelihood (ML) is the most widely used approach for statistical inference. Although it has the advantage of employing straightforward calculations, the ML approach lacks robustness, giving rise to spurious results and misleading conclusions. Researchers in epidemiology and a variety of other experimental and health sciences are becoming increasingly aware of this issue and are informed about the available alternatives for more reliable inference. What is robustness? Although it is intuitively clear what robustness should be, there is no unique statistical definition, in part because of the diverse aspects of robustness. The generally accepted notion is that a robust statistical procedure should be insensitive to changes not involving the parameters, but sensitive to changes in model parameters. For example, the ML approach is the most powerful for detecting changes in the parameters under the model. However, it is generally sensitive to model assumptions, yielding biased estimates and incorrect inference when ...

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