• Kofnov, A., Moosbrugger, M., Stankovič, M., Bartocci, E. and Bura, E. (2024). Ex- act and Approximate Moment Derivation for Probabilistic Loops With Non-Polynomial Assignments. ACM Transactions on Modeling and Computer Simulation (TOMACS). https://doi.org/10.1145/3641545 
• Song, M, Bura, E, Parzer, R, Pfeiffer, RM. Structured time-dependent inverse regression (STIR). Statistics in Medicine. 2023; 1- 19. doi: 10.1002/sim.9670
• Brune, B., Scherrer, W. and Bura, E. (2022). A state-space approach to time-varying reduced rank regression. Econometric Reviews, 1-23. doi:10.1080/07474938.2022.2073743
• Kofnov, A., Moosbrugger, M., Stankovic, M., Bartocci, E. and Bura, E. (2022). Moment-based Invariants for Probabilistic Loops with non-polynomial assignments. QEST 2022: the 19th International Conference on Quantitative Evaluation of SysTems In: Ábrahám, E., Paolieri, M. (eds) Quantitative Evaluation of Systems. QEST 2022. Lecture Notes in Computer Science, vol 13479.
• Karimi, A., Stankovic, M., Moosbrugger, M., Kovacs, L., Bartocci, E. and Bura, E. (2022). Distribution Estimation for Probabilistic Loops. QEST 2022: the 19th International Conference on Quantitative Evaluation of SysTems In: Ábrahám, E., Paolieri, M. (eds) Quantitative Evaluation of Systems. QEST 2022. Lecture Notes in Computer Science, vol 13479.
• Fertl, L., Bura, E. (2022), The ensemble conditional variance estimator for sufficient dimension reduction, Electron. J. Statist. 16(1): 1595-1634. DOI: 10.1214/22-EJS1994
• Fertl, L. and Bura, E. (2022), Conditional variance estimator for sufficient dimension reduction, Bernoulli 28(3): 1862-1891. DOI: 10.3150/21-BEJ1402
• Bura E. , Li B. (Eds.) (2021). Festschrift in Honor of R. Dennis Cook: Fifty Years of Contribution to Statistical Science. Springer, Cham, 2021, ISBN: 978-3-030-69008-3; 192 pp.
• Kapla, D., Fertl, L. and Bura, E. (2021). Fusing sufficient dimension reduction with neural networks, Computational Statistics & Data Analysis, Volume 168 (2022), doi: 10.1016/j.csda.2021.107390
• Bura, E., Forzani, L., Garcia Arancibia, R., Llop, P. and Tomassi, D. (2022). Sufficient dimension reductions for mixed predictors, Journal of Machine Learning Research, Vol.23, No.102, 1-47.
• Barbarino, A. and Bura, E. (2021). Forecasting Near-equivalence of Linear Dimension Reduction Methods in Large Panels of Macro-variables, Econometrics and Statistics, 1-18, doi: 10.1016/j.ecosta.2021.10.007
• Efstathia Bura, Bing Li, Lexin Li, Christopher Nachtsheim, Daniel Pena, Claude Setodji, Robert E. Weiss (2021). A Conversation with Dennis Cook. Statistical Science, Statistical Science, Vol. 36, No. 2, 328-337, doi: 10.1214/20-STS801
• Trindade, O.S., Berisha, T., Svoboda, P., Bura, E. and Mecklenbräucker, C. F. (2020). Assessment of Treatment Influence in Mobile Network Coverage on board High- Speed Trains. IEEE Access, v. 8, 162945-162960. doi: 10.1109/ACCESS.2020.3021647
• Pfeiffer. R., Kapla, D. B. and Bura, E. (2020). Least squares and maximum likelihood estimation of sufficient reductions in regressions with matrix-valued predictors, International Journal of Data Science and Analytics 11, 11-26. doi: 10.1007/s41060-020-00228-y.
• Pfeiffer, R., Wang, W. and Bura, E. (2019). Least Squares and Maximum Likelihood Estimation of Sufficient Reductions in Regression with Matrix Valued Predictors. In Proceedings of the 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Washington, DC, USA, 2019, pp. 135-144, doi: 10.1109/DSAA.201900028.
• Bura, E., Duarte, S., Forzani, L., Smucler, E., Sued, M. (2018). Asymptotic theory for maximum likelihood estimates in reduced-rank multivariate generalized linear models. STATISTICS, 52 (2018), 5, 1005-1024.
• Barbarino, A. and Bura, E. (2017). A Unified Framework for Dimension Reduction in Forecasting. Finance and Economics Discussion Series 2017-004, FED-Journal, Washington: Board of Governors of the Federal Reserve System, 004 (2017), 1-69.
• Tomassi, D., Forzani, L., Bura, E. and Pfeiffer, R. (2016). Sufficient dimension reduction for censored predictors. Biometrics, 73 (2017), 220-231.
• Barbarino, A. and Bura, E. (2015). Forecasting with sufficient dimension reductions. Finance and Economics Discussion Series 2015-074. Washington: Board of Governors of the Federal Reserve System, dx.doi.org/10.17016/FEDS.2015.074.
• Bura, E., Duarte, S. and Forzani, L. (2016). Sufficient reductions in regressions with exponential family inverse predictors. Journal of the American Statistical Association, Volume 111, Issue 515, s.1313-1329, doi: 10.1080/01621459.2015.1093944
• Bura, E. and Forzani, L. (2015). Sufficient reductions in regressions with elliptically contoured inverse predictors. Journal of the American Statistical Association, 110(509), 420-434.
• Bajeux-Besnainou, I., Bandara, W. and Bura, E. (2012). A Krylov Subspace Approach to Large Portfolio Optimization. Journal of Economic Dynamics and Control, 36(11), 1688-1699. (author order is alphabetical)
• Pfeiffer, R., Forzani, L. and Bura, E. (2012). Sufficient Dimension Reduction for Longitudinally Measured Predictors. Statistics in Medicine, Special Issue: Biomarker Working Group: Issues in the Design and Analysis of Epidemiological Studies with Biomarkers, 31(22), 2414-2427. doi: 10.1002/sim.4437.
• Gastwirth, J. L., Bura, E., and Miao, W. (2011). Some important statistical issues courts should consider in their assessment of statistical analyses submitted in class certification motions: implications for Dukes v. Wal-mart. Law, Probability and Risk, 10, 225-263. (special issue devoted to papers given at the 2011 conference on Quantitative Aspects of Justice and Proportionality edited by R. Bengez, S. Brewer, G. Sartori and V. Walker.)
• Bura, E., Gastwirth, J. L. and Hikawa, H. (2011). The Use of Peters-Belson Regression in Legal Cases. To appear in Nonparametric Statistical Methods and Related Topics A Festscrift in Honor of Professor P.K. Bhattacharya on the Occasion of his 80th Birthday. Edited by: Jiming Jiang, George G. Roussas, Francisco J. Samaniego. World Scientific Publ. Co.(WS).
• Bura, E. and Yang, J. (2011). Dimension Estimation in Sufficient Dimension Reduction: A Unifying Approach. Journal of Multivariate Analysis, 102, 130-142.
• Bura, E. and Gastwirth, J. L. (2010). Regression Analysis and Strengthening the SEC’s Efforts to Regulate Mutual Funds. Banking & Financial Services Policy Report, 29(6), 18-26.
• Hikawa, H., Bura, E. and Gastwirth, J. L. (2009). Local Linear Logistic Peters-Belson Regression and its application in employment discrimination cases. Statistics and its Interface, 3, 125-144.
• Bura, E. and Gastwirth, J. L. (2009). How accurate are the power calculations relied upon the SEC? Law, Probability and Risk, 8, 277-288; doi: 10.1093/lpr/mgp003.
• Bura, E., Zhmurov, A. and Barsegov, V. (2009). Density estimation methods for protein unfolding and unbinding data. The Journal of Chemical Physics, 130, 15-102. This article was selected for the January 15, 2009 issue of the Virtual Journal of Biological Physics Research.
• Pfeiffer, R. and Bura, E. (2008). A model free approach to combining diagnostic markers. Biometrical Journal, 50, 558-570.
• Bura, E. and Pfeiffer, R. (2008). On the distribution of the left singular vectors of a random matrix and its applications, Statistics and Probability Letters 58, 2275-2280.
• Bura, E., Klimov, D. K. and Barsegov, V. (2008). Analyzing Forced Unfolding of Protein Tandems by Ordered Variates: 2. Dependent Unfolding Times, Biophysical Journal, 94, 2516-2528.
• Bura, E., Klimov, D.K. and Barsegov, V. (2007). Analyzing Forced Unfolding of Protein Tandems by Ordered Variates: 1. Independent Unfolding Times, Biophysical Journal , 23 , 1100-1115.
• Bura, E. and Gastwirth, G.L. (2006).Assessing the Fairness of a Firm’s Allocation of Shares in Initial Public Offerings: Adapting a Measure from Biostatistics, Chance (2006) , 19(2) , 32-37.
• Yin, X. and Bura, E. (2006). Moment Based Dimension Reduction for Multivariate Response Regression, Journal of Statistical Planning and Inference , 136 , 3675-3688.
• Gastwirth, J.L., Modarres, R. and Bura, E. (2005).The Use of the Lorenz Curve, Gini Index and Related Measures of Relative Inequality and Uniformity in Securities Law, METRON (2005) , vol. LXIII , 451-469.
• Gastwirth, J. L., Bura, E. and Modarres, R. (2005).Statistical methods for assessing the fairness of the allocation of shares in initial public offerings, Law, Probability and Risk , 4 , 143-158.
• Hall, P. and Bura, E. (2004). Nonparametric Methods of Inference for Finite-State, Inhomogeneous Markov Processes, Bernoulli (2004), 10(5), 919-938.
• Bura, E. and Pfeiffer, R.M. (2003). Graphical methods for class prediction using dimension reduction techniques on DNA microarray data, Bioninformatics, 19, 1252-1258.
• Bura, E. and Cook, R.D. (2003). Rank Estimation in Reduced-Rank Regression, Journal of Multivariate Analysis, 87 , 159-176.
• Bura, E. and Cook, R.D. (2003). Assessing Corrections to the Weighted Chi-Squared Test for Dimension, Communications in Statistics: Simulation and Computation, 32 , 127-146.
• Bura, E. (2003). Using Linear Smoothers to Assess the Structural Dimension of Regressions, Statistica Sinica, 13, 143-162.
• Pfeiffer, R.M., Bura, E., Smith, A. and Reiter, J.L. (2002).Two Approaches to Mutation Detection Based on Functional Data, Statistics in Medicine, 21 , 3447-3464.
  • You can download the figures by clicking on the following , , ,
• Bura, E. and Cook, R.D. (2001). Estimating the Structural Dimension of Regressions via Parametric Inverse Regression, Journal of the Royal Statistical Society, Series B, 63 , 393-410.
  • The XLISP-STAT code for the dimension estimation polynomial inverse regression method can be downloaded by clicking here. To be able to use the code, you need to download arc first. Follow the instructions at the top of the file on how to load the lisp file into arc .
• Bura, E. and Cook, R.D. (2001). Extending SIR: The Weighted Chi-Square Test, Journal of the American Statistical Association , 96 , 996-1003.
• Bura, E. and Gastwirth, J.L. (2001). The Binary Regression Quantile Plot: Assessing the Importance of Predictors in Binary Regression Visually, Biometrical Journal, 43, 5-21.
• Hayek, L.-A. and Bura, E. (2001). On the Ends of the Taxon Range Problem, In Pattern from Process in the Fossil Record, Jackson, J.B.C., Lidgard, S. and McKinney, F.K. (eds), 2001. Chicago University Press.
• Bura, E. (1997). Dimension Reduction via Parametric Regression, In Y. Dodge (ed.), L1-Statistical Procedures and Related Topics, Institute of Mathematical Statistics Lecture Notes-Monograph Series (1997), v. 31, pp. 215-228. Hayward, CA: Institute of Mathematical Statistics.
• Cook, R.D. and Bura, E. (1997). Testing the Adequacy of Regression Functions, Biometrika 84, 949-956.

Non-refereed Publications:

• Bura, E., Peña, D., Li, L., Nachtsheim, C., Setodji, C., Weiss, R.E., Li, B. (2021). A Conversation with Dennis Cook. Statistical Science, 36(2), 328-337. 
• Bura, E. (2018). Sufficient Reductions of Predictors in the Exponential and Elliptically Contoured Distribution Families. International Mathematical News, 237, 17–25.
• Gastwirth, J. L., Miao, W. and Bura, E. (2012). Did the Supreme Court Appreciate the Statistical Evidence Pertaining to “Commonality” in Dukes v. Wal-Mart? Res Publica: Law and Social Policy Blog and Forum, Northwestern Journal of Law and Social Policy.
• Bura, E. and Gastwirth, J. L. (2010). Regression Analysis and Strengthening the SEC’s Efforts to Regulate Mutual Funds. Banking Financial Services Policy Report, 29 (6),18-26.