R Karunamuni, PhD

Professor, Faculty of Science - Mathematics & Statistical Sciences

Contact

Professor, Faculty of Science - Mathematics & Statistical Sciences
Email
R.J.Karunamuni@ualberta.ca

Overview

Research

Research Interests

My current research interests include efficient robust inference for parametric and semiparametric models, variable selection and high-dimensional data problems. 

Selected Publications

1. Q. Tang and R.J. Karunamuni (2016). Fast and accurate computation of kernel estimators. Computational Statistics and Data Analysis, 94, 49-62.

2. R.J. Karunamuni, Q. Tang and B. Zhao (2015). Robust and efficient estimation of effective dose. Computational Statistics and Data Analysis, 90, 47-60.

3. J. Wu and R.J. Karunamuni (2015). Profile Hellinger distance estimation. Statistics, 49, 711-740. 

4. Q. Tang and R.J. Karunamuni (2013). Minimum Hellinger distance estimation in a finite mixture regression model. Journal of Multivariate Analysis, 120, 185-204.

5. S. Zhao, P. Li and R. Karunamuni (2013). Blocked two-level regular factorial designs with weak minimum aberration. Biometrika, 100, 249-253.

6. S. Zhao, P. Li, R. Zhang and R. Karunamuni (2013). Construction of blocked two-level regular designs with general minimum lower order confounding. Journal of Statistical Planning and Inference, 143, 1082-1090.

7. J. Wu and R.J. Karunamuni (2012). Efficient Hellinger distance estimates for semiparametric models. Journal of Multivariate Analysis, 107, 1-23.

8. R.J. Karunamuni and J. Wu (2011). One-step minimum Hellinger distance estimation. Computational Statistics and Data Analysis, 55, 3148-3164.

9. J. Kolacek and R.J. Karunamuni (2011). A generalized reflection method for kernel distribution and hazard function estimation. Journal of Applied Probability and Statistics, 6, 73-85.

10. J. Wu, R.J. Karunamuni and B. Zhang (2010). Minimum Hellinger distance estimation in a two-sample semiparametric model. Journal of Multivariate Analysis, 101, 1102-1122.

11. R.J. Karunamuni, J. Li and J. Wu (2010). Robust empirical Bayes tests for continuous distributions. Journal of Statistical Planning and Inference, 140, 268-282.

12. S. Zhang and R.J. Karunamuni (2010). Boundary performance of the beta-kernel estimator. Journal of Nonparametric Statistics, 22, 81-104.

13. J. Wu and R.J. Karunamuni (2009). On minimum Hellinger distance estimation. The Canadian Journal of Statistics, 37, 514-533.

14. R.J. Karunamuni and J. Wu (2009). Minimum Hellinger distance estimation in a nonparametric mixture model. Journal of Statistical Planning and Inference, 139, 1118-1133. 

15. S. Zhang and R.J. Karunamuni (2009). Deconvolution boundary kernel method in nonparametric density estimation. Journal of Statistical Planning and Inference, 139, 2269-2283. 

For rest of the articles see: https://www.dropbox.com/s/txa59j43k42umr0/Research%20Papers%20of%20R.J.%20Karunamuni.pdf


Courses

STAT 361 - Sampling Techniques

Simple random sampling from finite populations, stratified sampling, regression estimators, cluster sampling. Prerequisite: STAT 266, or STAT 235 with consent of the Department. Note: This course may only be offered in alternate years.

Winter Term 2021
STAT 372 - Mathematical Statistics

Laws of large numbers, weak convergence, some asymptotic results, delta method, maximum likelihood estimation, testing, UMP tests, LR tests, nonparametric methods (sign test, rank test), robustness, statistics and their sensitivity properties, prior and posterior distributions, Bayesian inference, conjugate priors, Bayes estimators. Prerequisite: STAT 266.

Fall Term 2020
STAT 566 - Methods of Statistical Inference

An introduction to the theory of statistical inference. Topics to include exponential families and general linear models, likelihood, sufficiency, ancillarity, interval and point estimation, asymptotic approximations. Optional topics as time allows, may include Bayesian methods, Robustness, resampling techniques. This course is intended primarily for MSc students. Prerequisite: STAT 471 or consent of Department.

Fall Term 2020

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