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## Statistical Theory and the Real World |

Standard statistical analysis
mostly concerns the situation in which one observes repeated outcomes of a
particular process, and the goal is to infer hypotheses about the distribution
of these outcomes; such hypotheses may be parameters, confidence intervals or
structural properties such as (in)dependence relations. But often the relation
between the data, the hypotheses and the real world is not nearly as convenient
as that: either 1. the data selection process; or 2. the data measurement process
may play a crucial role; or 3. the interest is in hypotheses
about what happens when intervening in the process, whereas the data are obtained by just letting the
process run its course. A prime example of the first case and second case can
be found in statistical analysis of data that are coarsened (e.g., missing, or
censored) in a nonrandom fashion. All three complications, with an emphasis on
the third, play a crucial role in causal inference; again, all three, with
emphasis on the second, play a role when data are generated by a
quantum-mechanical process. And a prime application area that again involves
all three aspects is forensic statistics. This workshop is planned to be
held on the occasion of the retirement of Richard Gill, Professor of Statistics
at Leiden University and well-known for his fundamental contributions to these
tricky areas of statistics that involve complex interaction with reality. While
we plan to invite some of Richard’s collaborators, we emphasize that the
workshop’s focus is on original research in the topics mentioned above — we only invite speakers who
are still highly active, and a substantial fraction of talks will be given by younger researchers who
will be asked to report on the latest developments in their respective fields,
in a manner that is accessible to all the participants [Back] |