Statistical data science represents an interdisciplinary field that merges the rigour of probability theory and statistical inference with modern computational tools to draw insights and make ...

If program staff suspects you may have used AI tools to complete assignments in ways not explicitly authorized or suspect other violations of the honor code, they will contact you via email. Be sure ...

Moment inequalities provide a framework to extract reliable information from models where parameters cannot be precisely identified. Owing to either optimisation behaviour by individual agents or ...

Confidence intervals are computed from a random sample and therefore they are also random. The long run behavior of a 95% confidence interval is such that we’d expect 95% of the confidence intervals ...

In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...

Successful completion of this course demonstrate your achievement of the following learning outcomes for the MS-DS program: Define a composite hypothesis and the level of significance for a test with ...

In this paper we provide the basis for new methods of inference for max-stable processes ξ on general spaces that admit a certain incremental representation, which, in important cases, has a much ...

This is a preview. Log in through your library . Abstract We present a general approach to statistical problems with criteria based on probabilities of large deviations. Our main idea, which ...

The most widely used statistical inference procedures were invented more than 10 years ago and are not well suited for handling state-of-the art high-resolution neuroimaging data. MRI technology ...

Eleanor has an undergraduate degree in zoology from the University of Reading and a master’s in wildlife documentary production from the University of Salford.View full profile Eleanor has an ...