Dimension Reduction in Regression
R. D. Cook, L. Forzani and A. Rothman. Estimating Sufficient Reductions of the Predictors in Abundant High-Dimensional Regression. To appear in Annals of Statistics.
R. D. Cook and L. Forzani. On the mean and variance of the generalized inverse of a singular Wishart matrix. Electronic Journal of Statistics.
R. D. Cook, L. Forzani and D. Tomassi. LDR: A Package for Likelihood-Based Sufficient Dimension Reduction .
R. D. Cook and L. Forzani. Principal Fitted Components for Dimension Reduction in Regression. Statistical Science.
R. D. Cook, B. Li, F. Chiaromonte and L. Forzani. Sufficient and Efficient Dimension Reduction based on Normal Inverse Models. Preprint.
R. D. Cook and L. Forzani. Likelihood-based Sufficient Dimension Reduction. JASA.
R. D. Cook and L. Forzani. Covariance reducing models: An alternative to spectral modelling of covariance matrices. Biometrika.
Thesis:
My thesis proposal: Principal Components for Regression: a conditional point of view Advisor: R. Dennis Cook. School of Statistics. University of Minnesota. January 2007.
PhD Thesis: Sufficient Dimension Reduction Based on Normal and Wishart Inverse Models. Advisor: R. Dennis Cook. School of Statistics. University of Minnesota. December 2007.
Degrees of freedom for normal model with non constant variance