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The University of Southampton
Southampton Statistical Sciences Research Institute

Analysing data from optimal mixed level supersaturated designs using group screening Seminar

Date:
17 May 2012
Venue:
Building 54 Room 10037

For more information regarding this seminar, please email Mrs Jane Revell at j.revell@southampton.ac.uk .

Event details

Statistics research seminars

Supersaturated designs (SSDs) are used for screening out the important factors from a large set of potentially active variables. The huge advantage of these designs is that they reduce the experimental cost drastically, but their critical disadvantage is the high degree of confounding among factorial effects. In this contribution, we focus on mixed-level factorial designs which have different numbers of levels for the factors. Such designs are often useful for experiments involving both qualitative and quantitative factors. When analyzing data from SSDs, as in any decision problem, errors of various types must be balanced against cost. In SSDs, there is a cost of declaring an inactive factor to be active (i.e. making a Type I error), and a cost of declaring an active effect to be inactive (i.e. making a Type II error). Type II errors are usually considered much more serious than Type I errors. We present a group screening method for analysing data from E(f_{NOD})-optimal mixed-level supersaturated designs possessing the equal occurrence property. Based on the idea of the group screening methods, the f factors are sub-divided into g ?group-factors?. The ?group-factors? are then studied using the penalized likelihood methods involving a factorial design with orthogonal or near-orthogonal columns. The penalized likelihood methods indicate which ?group factors? have a large effect and need to be studied in a follow-up experiment. We will compare various methods in terms of Type I and Type II error rates using a simulation study.

Keywords and phrases: Group screening method, Data analysis, Penalized least squares, Super-saturated design.

Speaker information

Kalliopi Mylona ,Lecturer in statistics

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