Research and Publications
My research interests include:
Statistics, Applied Statistics, Statistical Computing, Multivariate Analysis,
Multivariate Inference, Goodness-of-Fit, Nonlinear Dependence, Statistical Learning,
Cluster Analysis and Classification, Computational Statistics, and Energy Statistics.
Research Gate Profile
Statistical Computing With R, Second Edition, Chapman & Hall/CRC, March 6, 2019.
Statistical Computing with R, Chapman & Hall/CRC (2008).
R by Example (with Jim Albert), Springer Use R! Series (2012).
Energy Statistics, Chapman & Hall/CRC: (forthcoming)
Research and software related to E-statistics
E-statistics (energy statistics, joint work with G. J. Szekely)
refers to a class of tests and statistics
based on Euclidean distances. Applications include testing
multivariate normality, multivariate distance components and
k-sample test for equal distributions, hierarchical clustering by e-distances,
multivariate independence tests, distance correlation, goodness-of-fit tests.
Gabor J. Szekely,
National Science Foundation,
Maria L. Rizzo,
Bowling Green State University.
R software: Energy statistics are implemented in the contributed
energy for R.
See GitHub account mariarizzo for
updates under development.
Songzi Li and Maria L. Rizzo (2017). K-groups: A Generalization of K-means Clustering,
ArXiv e-prints, 1711.04359,
M. L. Rizzo and J. T. Haman (2016).
Expected distances and goodness-of-fit for the asymmetric Laplace distribution,
Statistics & Probability Letters,
Volume 117, pp. 158-164, ISSN 0167-7152,
G. J. Szekely and M. L. Rizzo (2017). The Energy of Data,
The Annual Review of Statistics and Its Applications. Extended Review.
4:447-479. , doi: 10.1146/annurev-statistics-060116-054026
G. J. Szekely and M. L. Rizzo (2015).
“Partial Distance Correlation”, in Nonparametric Statistics-2nd ISNPS Conference,
Cádiz, 2014. Springer. 179-190.
G. J. Szekely and M. L. Rizzo.
Partial Distance Correlation,
Proceedings of the 2nd International Conference
on Nonparametric Statistics, Springer (to appear).
M. L. Rizzo and G. J. Szekely (2016).
Energy Distance, WIRES Computational Statistics,
Wiley, Volume 8 Issue 1, 27-38.
Available online Dec., 2015, doi: 10.1002/wics.1375.
G. J. Szekely and M. L. Rizzo (2014).
Partial distance correlation with methods for dissimilarities,
Annals of Statistics, 42/6, 2382-2412.
C. D. Yenigun and M. L. Rizzo (2014).
Variable Selection in Regression using Maximal Correlation and
Journal of Statistical Computation and Simulation.
March, 2014. DOI: 10.1080/00949655.2014.895354
- G. J. Szekely and M. L. Rizzo (2013).
Energy statistics: statistics based on distances.
Journal of Statistical Planning and Inference
Volume 143, Issue 8, August 2013, pp. 1249-1272.
- G. J. Szekely and M. L. Rizzo (2013).
The distance correlation t-test of independence in high dimension.
Journal of Multivariate Analysis, Volume 117, pp. 193-213.
- G. J. Szekely and M. L. Rizzo (2012).
On the uniqueness of distance covariance.
Statistics & Probability Letters, Volume 82, Issue 12, 2278-2282.
- Maria L. Rizzo and Gabor J. Szekely (2010).
DISCO Analysis: A Nonparametric Extension of Analysis of Variance,
Annals of Applied Statistics Vol. 4, No. 2, 1034-1055.
- Gabor J. Szekely and Maria L. Rizzo (2009). Brownian Distance
Annals of Applied Statistics,
Vol. 3, No. 4, 1236-1265.
- Gabor J. Szekely and Maria L. Rizzo (2009). Rejoinder: Brownian Distance.
Covariance, Annals of Applied Statistics, Vol. 3, No. 4, 1303-1308.
- Maria. L. Rizzo (2009). New Goodness-of-Fit Tests for Pareto Distributions,
ASTIN Bulletin: Journal of the International Association of Actuaries,
39/2, 691-715. PDF
- G. J. Szekely, M. L. Rizzo, and N. K. Bakirov (2007).
Measuring and Testing Independence by Correlation of Distances, Annals of Statistics,
Vol. 35 No. 6, pp. 2769-2794.
Bakirov, N. K., Rizzo, M. L., and Szekely, G. J. (2006).
A Multivariate Nonparametric Test of Independence, Journal of Multivariate Analysis
Volume 97, Issue 8 , September 2006, Pages 1742-1756
- Szekely, G. J. and Rizzo, M. L. (2005) Hierarchical Clustering
via Joint Between-Within Distances: Extending Ward's Minimum Variance Method,
Journal of Classification, 22(2) 151-183.
- Szekely, G. J. and Rizzo, M. L. (2005) A New Test for
Journal of Multivariate Analysis,
- Szekely, G. J. and Rizzo, M. L. (2004b) Mean Distance Test of Poisson Distribution,
Statistics and Probability Letters, 67/3, 241-247
- Rizzo, M. L. (2003) Hierarchical Clustering Based on a Generalized
Measure of Homogeneity,
2003 Proceedings of the Joint Statistical Meetings, American Statistical
Association, Section for Physical and Engineering Sciences [CD-ROM],
Alexandria, VA: American Statistical Association.
- Szekely, G. J. and Rizzo, M. L. (2004) Testing for Equal
Distributions in High Dimension, InterStat, Nov. (5).
- M. L. Rizzo (2005) Minimum Energy Clustering
Proceedings of Interface/Classification Society of North America,
Joint Annual Meeting, 2005.
- Rizzo, M. L. (2002a). A Test of Homogeneity for Two Multivariate Populations,
2002 Proceedings of the American Statistical Association, Physical and Engineering
Sciences Section [CD-ROM], Alexandria, VA: American Statistical Association.
- Rizzo, M. L. (2002b). A New Rotation Invariant Goodness-of-Fit Test,
Ph.D. dissertation, Bowling Green State University.
- Szekely, G. J. (2002) E-statistics: the Energy of Statistical Samples,
Technical Report No. 02-16, Bowling Green State University, Department
of Mathematics and Statistics, October 2002. PDF
- Szekely, G. J. (2000) E-statistics: Energy of
Statistical Samples, Bowling Green State University, Department of
Mathematics and Statistics Technical Report No. 03-05.
- Szekely, G. J. (1989) Potential and Kinetic Energy in Statistics,
Lecture Notes, Budapest Institute of Technology (Technical University).
R is a free software environment
for statistical computing and graphics, available at the
Archive Network (CRAN)..
This software is distributed under
Public License Version 2, or later. See
COPYING for the license.
Questions or comments on software: Maria Rizzo, email address above
[go to References]
Some functions in energy have been translated to Matlab.