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Theoretical properties of Cook's Principal Fitted Components algorithm

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I will talk about how to reduce the dimension of the linear regression space. In particular, I will consider the theoretical properties of the estimators resulting from Dennis Cook’s Principal Fitted Components algorithm. I will give sufficient conditions for root(n)-consistency and explain some of the simulation results in Cook’s Fisher Lecture. I will argue further that, under Cook’s model at least, the PFC algorithm outperforms the more standard Principal Components algorithm. (paper to appear in Electronic Journal of Statistics)

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Large Lecture Theatre, Botany Building, Downing Site Venue to be disclosed Lloyd Room, Christ's College, Saint Andrews Street, Cambridge CB2 3BU

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