Statistics

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My interests as a statistician cover multivariate analysis and regression (including dimension reduction, supervised and unsupervised classification, non-parametric tools and parametric regression modeling), computational techniques (including re-sampling, perturbation and permutation schemes for the empirical assessment of significance), approaches to the analysis of high-dimensional and under-sampled data (which are very common in genomics applications), and Markov models (as applied to genomic sequences and alignments). With funding from the NSF, R. Dennis Cook (Statistics, U of MN), Bing Li (Statistics, Penn State) and I have pursued research on Sufficient Dimension Reduction, a body of theory and methods for handling high-dimensional regression and classification problems, which is also closely related to graphics and data visualization.

REPRESENTATIVE PUBLICATIONS

  • Lee K.Y., Li B. and Chiaromonte F. (2013) A general theory of nonlinear sufficient dimension reduction: formulation and estimation. Annals of Statistics 41(1), 221-249. doi:10.1214/12-AOS1071
  • Chiaromonte F. and Taylor J. (2010) Information Based Agglomerative Segmentation in Metric Spaces. Journal of the Indian Society of Agricultural Statistics, 64(1), 33-44.
  • Cook R.D., Li B. and Chiaromonte F. (2010) Envelope models for parsimonious and efficient multivariate linear regression. Discussion paper. Statistica Sinica, 20(3), 927-910 (including comments and rejoinder).
  • Tyekucheva S. and Chiaromonte F. (2008) Augmenting the bootstrap to analyze high dimensional genomic data (invited article with discussion). Test, 17, 1-18 (article), 47-55 (rejoinder).
  • Cook R.D., Li B. and Chiaromonte F. (2007) Dimension reduction in regression without matrix inversion. Biometrika, 94, 569-584.
  • Li B., Zha H. and Chiaromonte F. (2005) Contour regression: a general approach to dimension reduction. Annals of Statistics, 33, 1580-1616.
  • Li B., Cook R.D. and Chiaromonte F. (2004) Dimension reduction for the conditional mean in regressions with categorical predictors. Annals of Statistics, 30, 1636-1668.
  • Chiaromonte F., Cook R.D. and Li B. (2002) Sufficient dimension reduction in regressions with categorical predictors. Annals of Statistics, 30, 475-497
  • Chiaromonte F. and Cook R.D. (2002) Sufficient dimension-reduction and graphics in regression. Annals of the Institute of Statistical Mathematics, 54, 768-795.
  • Chiaromonte F. and Martinelli J.A. (2002) Dimension reduction strategies for analyzing global gene expression data with a response. Mathematical Biosciences, 176, 123-144.

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