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Modern challenges of curve modelling: inverse problems and qualitative constraints

Research workshop: 7-9 November 2007, Bristol, UK

The scope of the workshop will be inverse problems, and curve estimation under qualitative constraints.

Within inverse problems, there will be a particular focus in this workshop on the problem of measurement error, where the goal is to develop statistical methods taking this into account. Measurement error arises in many applications but has recently been most important in analysing medical and econometrics data.

Qualitative assumptions have successfully been used in improving the performance of existing regression methodology and there is clear potential for further improvement. Constraints such as monotonicity or bounds on the number of local extrema are easy to interpret and can be used to replace quantitative assumptions such as smoothness which are often difficult to justify.

There will be about 14 invited speakers, several contributed paper sessions and a poster session.

Participation

If you are interested in participating or giving a contributed talk/poster, please write to sustain-admin 'AT' bristol.ac.uk. There is no registration fee but the number of participants is limited. Lunches, tea and coffee are provided to participants, but you have to fund your own accommodation and travel. Information on accommodation near the workshop venue will be provided to participants at the time they are accepted.

We will have some limited funds to support expenses for junior researchers (graduate students, and researchers within 3 years of receiving their Ph.D. degree).

Organisation

This workshop is run by the Statistics Group of the Department of Mathematics, University of Bristol. This event is one of the activities of SuSTaIn which will be organised by the Statistics group over the next five years. It commences at 9 a.m. on Wednesday 7 November, and will conclude with lunch on Friday 9 November.

The workshop is organised by Aurore Delaigle, Arne Kovac, Peter Green and Azita Ghassemi.