Suthakaran Ratnasingam

Research

Areas of Interest

  • Change Point Analysis
  • Sequential Data Analysis
  • Statistical Inferences
  • High-dimensional Data Analysis
  • Bounds and Inequalities

I am happy to work with undergraduate and graduate students. If you are interested in a research project or thesis, please contact me.

19) Gu, C * ., & Ratnasingam, S * . (2025), Change Point Detection in SCAD-Penalized Dynamic Panel Models, Sequential Analysis, 44(4), 377-403. [pdf]

18) From, S. G., & Ratnasingam, S. (2025), New Upper and Lower Bounds for the Upper Incomplete Gamma Function, Results in Applied Mathematics 25, 100552. [pdf]

17) Ratnasingam, S., & Gamage, R. D. P. (2025), Empirical Likelihood Change Point Detection in Quantile Regression Models, Computational Statistics 40, 999–1020. [pdf]

16) Li, M., Ratnasingam, S., Tian, Y., & Ning, W. (2024), Change Point Detection in Length-biased Lognormal Distribution, Communications in Statistics - Simulation and Computation, 54(11), 4605-4622. [pdf]

15) Ratnasingam, S., Wallace, S.+, Amani, I.+, & Romero, J.+ (2024), Nonparametric Confidence Intervals for Generalized Lorenz Curve Using Modified Empirical Likelihood, Computational Statistics 39, 3073–3090. [pdf]

14) Ratnasingam, S., & Muñoz-Lopez, J.+ (2023), Distance Correlation-Based Feature Selection in Random Forest, Entropy 25(9), 1250. [pdf]

13) Gu, C * ., & Ratnasingam, S * (2023), Real-Time Change Point Detection in Linear Models Using the Ranking Selection Procedure, Sequential Analysis 42(2), 129-149. [pdf]

12) Karunanithy, R., Ratnasingam, S., Holland, TE., & Sivakumar, P. (2023), Sensitive Detection of Human Epididymis Protein-4 (HE4) Ovarian Cancer Biomarker through Sandwich Type Immunoassay Method with Laser-Induced Breakdown Spectroscopy, Bioconjugate Chemistry 34(3), 501–509. [pdf]

11) Ratnasingam, S., & Ning, W. (2023), Change Point Detection in Linear Failure Rate Distribution Under Random Censorship, Journal of Statistical Theory and Practice 17(1), 1-22. [pdf]

10) Ratnasingam, S., & Ning, W. (2023), Confidence Intervals of Mean Residual Life Function in Length-Biased Sampling Based on Modified Empirical Likelihood, Journal of Biopharmaceutical Statistics 33(1), 114-129. [pdf]

9) From, S. G., & Ratnasingam, S. (2022), Some Efficient Closed-Form Estimators of the Parameters of the Generalized Pareto Distribution, Environmental and Ecological Statistics 29(4), 827–847. [pdf] [R Package]

8) From, S. G., & Ratnasingam, S. (2022), Some New Inequalities for the Beta Function and Certain Ratios of Beta Functions, Results in Applied Mathematics 15, 100302. [pdf] [R Package]

7) Li, M., Ratnasingam, S., & Ning, W. (2022), Empirical-Likelihood-Based Confidence Intervals for Quantile Regression Models with Longitudinal Data, Journal of Statistical Computation and Simulation 92(12), 2536-2553. [pdf]

6) Ratnasingam, S., & Ning, W. (2021), Monitoring Sequential Structural Changes in Penalized High-Dimensional Linear Models, Sequential Analysis 40(3), 381-404. [pdf]

5) From, S. G., & Ratnasingam, S. (2021), Some New Bounds for Moment Generating Functions of Various Life Distributions Using Mean Residual Life Functions, Journal of Statistical Theory and Practice 15(2), 1-14. [pdf]

4) Ratnasingam, S., & Ning, W. (2021), Modified Information Criterion for Regular Change Point Models Based on Confidence Distribution, Environmental and Ecological Statistics 28(2), 303-322. [pdf]

3) Ratnasingam, S., & Ning, W. (2021), Sequential Change Point Detection for High-Dimensional Data Using Nonconvex Penalized Quantile Regression, Biometrical Journal 63(3), 575-598. [pdf] [supplementary]

2) Ratnasingam, S., & Ning, W. (2020), Confidence Distributions for Skew Normal Change-Point Model Based on Modified Information Criterion, Journal of Statistical Theory and Practice 14(3), 1-21. [pdf]

1) From, S. G., & Ratnasingam, S. (2016), Some New Refinements of the Arithmetic, Geometric and Harmonic Mean Inequalities with Applications, Applied Mathematical Sciences 10(52), 2553-2569. [pdf]

* - denotes joint first authors;
+ - denotes undergraduate/graduate students who work with me.