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Likelihood-based inference for complex data structures

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Statistical inference based on the likelihood function is well-established as the method of choice for a wide range of applications. As the structure of available data becomes increasingly complex, for example due to intricate sampling schemes, or the measurement of an extremely large number of variables, new developments in likelihood inference are needed. I will discuss some aspects of the current research in adapting likelihood inference to these settings, with particular reference to specific application areas.

This talk is part of the Kuwait Foundation Lectures series.

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