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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)

This talk is part of the Statistics series.

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