STATISTICS & BIOINFORMATICS METHODS

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REPRESENTATIVE PUBLICATIONS

  • Cremona M.A., Xu H., Makova K.M., Reimherr M., Chiaromonte F., Madrigal P. (2019) Functional data analysis for computational biology. Bioinformatics. doi.org/10.1093/bioinformatics/btz045
  • Yao W., Nandy D., Lindsay B.G., Chiaromonte F. (2018). Covariate Information Matrix for Sufficient Dimension Reduction. Journal of the American Statistical Association. doi.org/10.1080/01621459.2018.1515080
  • Cremona M.A., Pini A., Cumbo F., Makova K.D., Chiaromonte F. and Vantini S. (2018) IWTomics: testing high-resolution sequence-based “Omics” data at multiple locations and scales. Bioinformatics. doi.org/10.1093/bioinformatics/bty090
  • Liu Y., Chiaromonte F. and Li B. (2017) Structured Ordinary Least Squares: a sufficient dimension reduction approach for regressions with partitioned predictors and heterogeneous units. Biometrics doi:10.1111/biom.12579. R-package deposited on CRAN.
  • Bartolucci F., Chiaromonte F., Kuruppumullage Don P. and Lindsay B.G. (2016) Composite likelihood inference in a discrete latent variable model for two-way “clustering-by-segmentation” problems. Journal of Computational and Graphical Statistics doi:10.1080/10618600.2016.1172018.
  • Liu Y., Chiaromonte F., Ross H., Malhotra R., Elleder D. and Poss M. (2015) Error correction and statistical analyses for intra-host comparisons of feline immunodeficiency virus diversity from high-throughput sequencing data. BMC Bioinformatics 16(202). doi: 10.1186/s12859-015-0607-z.
  • Goldstein J., Haran M., Simeonov I., Fricks J. and Chiaromonte F. (2015). An attraction-repulsion point process model for respiratory syncytial virus infections. Biometrics. 71(2), 376–385.
  • Chiaromonte F. and Makova K.D. (2014). Using statistics to shed light on the dynamics of the human genome: a review. Advances in Complex Data Modeling and Computational Methods to Statistics, Contributions in Statistics. A. Paganoni and P. Secchi (eds), Springer Intl Publishing, SW. 69-85.
  • Kuruppumullage Don P., Lindsay B. and Chiaromonte F. (2014). Model-based block clustering with EM algorithm (reviewed; 2014 student paper award finalist, ASA Nonparametric Statistics Section).
  • 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.
  • 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), 910-927.
  • Kosakovsky Pond S., Wadhawan S., Chiaromonte F., Ananda G., Chung W.Y., Taylor J., Nekrutenko A. and The Galaxy Team. (2009) Windshield splatter analysis with the Galaxy metagenomic pipeline. Genome Research, 19, 2144-2153.
  • Tyekucheva S. and Chiaromonte F. (2008) Augmenting the bootstrap to analyze high dimensional genomic data. Invited discussion article 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.
  • Taylor J., Tyekucheva S., King D.C., Hardison R., Miller W. and Chiaromonte F. (2006) ESPERR: Learning strong and weak signals in genomic sequence alignments to identify functional elements. Genome Research, 16, 1596-1604.
  • Li B., Zha H. and Chiaromonte F. (2005) Contour regression: a general approach to dimension reduction. Annals of Statistics, 33(4), 1580-1616.
  • Kolbe D., Taylor J., Elnitski L., Eswara P., Li J., Miller W., Hardison R.C. and Chiaromonte F. (2004) Regulatory potential scores from genome-wide 3-way alignments of human, mouse and rat. Genome Research, 14, 700-707.
  • 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.
  • Li B., Zha H. and Chiaromonte F. (2004) Linear contour learning: a method for supervised dimension reduction. Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence. ACM International Conference Proceeding Series. 349-356.
  • Chiaromonte F., Bing Yap V. and Miller W. (2002) Scoring pairwise genomic sequence alignments. Proceedings of the Pacific Symposium on Biocomputing 2002.
  • Chiaromonte F., Martinelli J.A. (2002) Dimension reduction strategies for analyzing global gene expression data with a response. Mathematical Biosciences, 176 (1),123-144.
  • Chiaromonte F., Cook R.D. and Li B. (2002) Sufficient dimension reduction in regressions with categorical predictors. Annals of Statistics, 30(2). 475-497
  • Chiaromonte F. and Cook R.D. (2002) Sufficient dimension-reduction and graphics in regression. Annals of the Institute of Statistical Mathematics, 54(4) 768-795.
  • Chiaromonte F. (2001). Graphics and sufficient dimension reduction with continuous and categorical predictors. Modelli Complessi e Metodi Computazionali Intensivi per la Stima e la Previsione, C. Provasi (ed), Cleup, Padova ITALY. 39-44.
  • Chiaromonte F. (1998). On multivariate structures and exhaustive reductions. Computing Science and Statistics, 30, S. Weisberg (ed), Interface Foundation of North America, Fairfax Station VA, 204-213.
  • Chiaromonte F. (1997). A reduction paradigm for multivariate laws. L1 Statistical Procedures and Related Topics, Y. Dodge (ed), Institute of Mathematical Statistics Monograph Series, Hayward CA, 229-240.

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