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Tatjana Miljkovic
Associate Professor, Actuarial Science Advisor
Statistics

Tatjana Miljkovic

Contact Info

513-529-3299
Statistics, DSB 324
Google Scholar Link
ResearchGate

Current Position and Interests

Tatjana Miljkovic is an associate professor and actuarial science adviser in the Department of Statistics at Miami University. She brings many years of industry experience to the classroom, which significantly enhances her ability to mentor and advise students successfully. Her primary research interests include actuarial science and applied statistics, focusing on mixture modeling and clustering, applications of the EM algorithm, tail risk measures and capital requirements in insurance, and statistical applications in health and climate change. Additionally, as a statistician, she has collaborated on numerous interdisciplinary projects with colleagues from various research fields. In her free time, Tatjana enjoys cooking, swimming, and traveling.

Courses Taught at Miami

  • STA 271  Intro to Actuarial Science
  • STA 301  Applied Statistics
  • STA 363  Intro to Statistical Modeling
  • STA 401/501  Probability
  • STA 462/562  Inferential Statistics
  • STA 463/563  Regression Analysis
  • STA 466/566  Experimental Design Methods
  • STA 477/677  Independent Studies 
  • STA 483/583  Analysis of Forecasting Systems
  • STA 651  Generalized Linear Models
  • STA 652  Computational Statistics with the EM Algorithm
  • STA 660 Practicum In Data Analysis 

Publications

[32]  Miljkovic, T. and Wang, P. (2025) A Dimension Reduction Assisted Credit Scoring Method for Big Data with Categorical Features, Financial Innovation. (In Press).

[31] Vatsa, P., Miljkovic, T., and Miljkovic, D. (2024) Price discovery redux: analyzing energy spot and futures prices using a dynamic programming approach, Energy Economics. Vol. 140, pp. 107965. DOI:10.1016/j.eneco.2024.107965

[30] Bae T., Miljkovic, T. (2024) Loss Modeling with the Size-biased Lognormal Mixture and the Entropy Regularized EM Algorithm. Insurance: Mathematics and Economics. DOI: 10.1016/j.insmatheco.2024.05.003

[29] Miljkovic, T. and Chen, Y. (2023) U.S. Violent Manner of Death Mortality by Race and Ethnicity. The Society of Actuaries Research Institute. pp. 1–71.

[28] **Vorpe K., *Hessinger S., *Poth R., and Miljkovic, T. (2023) Clustering Regions with Dynamic Time Warping to Model Obesity Prevalence Disparities in the United States. Journal of Applied Statistics. DOI: 10.1080/02664763.2023.2192445

[27] Grün, B. and Miljkovic, T., (2022). The Automated Bias-Corrected and Accelerated Bootstrap Confidence Intervals for Risk Measures. North American Actuarial Journal, pp.1-20.

[26] *Woods, T. and Miljkovic, T., (2022). Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis. Risks, 10(10), p.197.

[25] Miljkovic, T. and Grün, B. (2021), Using Model Averaging to Determine Suitable Risk Measure Estimates. North American Actuarial Journal, pp.1-18.

[24] Miljkovic, T. and Chen, Y. (2021), A new computational approach for estimation of the Gini index based on grouped data. Computational Statistics. pp. 1-23

[23]Miljkovic, T. and Wang, X., (2021). Identifying subgroups of age and cohort effects in obesity prevalence. Biometrical Journal, 63(1), pp.168-186.

[22]**Počuča, N., Jevtić, P., McNicholas, P.D. and Miljkovic, T., (2020). Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models. Insurance: Mathematics and Economics, 94, pp.79-93.

 [21] Miljkovic, T., **Causey, R., Jovanovic, M. (2020), Assessing the Performance of Confidence Intervals for High Quantiles of Burr XII and Inverse Burr Mixtures. Communication in Statistics- Simulation and Computation. pp. 1--23. DOI: 10.1080/03610918.2020.1747075

[20] Michael, S., Miljkovic, T., Melnykov, V. (2020), Mixture modeling of insurance loss data with multiple level partial censoring. Advances in Data Analysis and Classification. DOI:10.1007/s11634-020-00391-x

[19] *Yu, M. *Krehbiel, M., *Thompson, S., Miljkovic, T. (2020), Exploration of Gender in the Actuarial Research Community with Advanced Data Science Tools. Scientometrics. 123(2) , pp.767--789. Springer. DOI: 10.1007/s11192-020-03412-w

[18] Grün, B. and Miljkovic, T. (2019), Extending Composite Loss Models Using a General Framework of Advanced Computational Tools. Scandinavian Actuarial Journal. Vol 2019, Issue 8, pp. 642--660.

[17] **Blostein, M. and Miljkovic, T. (2019), On modeling left-truncated loss data using mixtures of distributions. Insurance: Mathematics & Economics. Vol 85, pp. 35--46.

[16] **Daawin, P., Kim, S., and Miljkovic, T. (2019), Predictive Modeling of Obesity Prevalence for the U.S. Population. North American Actuarial Journal. Vol 23, Issue 1, pp. 64--81.

[15] Chen, YJ. and Miljkovic, T. (2019), From grouped to de-grouped data: a new approach in distribution fitting for grouped data. Journal of Statistical Computation and Simulation. Vol 89, Issue 2, pp.272--291.

[14] Miljkovic, T., Miljkovic, D., and Maurer, K. (2018), Examining the Impact on Mortality Arising from Climate Change: Important Findings for the Insurance Industry. European Actuarial Journal. Vol 8, Issue 2, pp.363--381.

[13] Miljkovic, T. and Fernandez, D. (2018), On Two Mixture-Based Clustering Approaches Used in Modeling an Insurance Portfolio. Risks. Vol. 6, Issue 2, pp.57--75.

[12] Zhang, J. and Miljkovic, T. (2018), Ratemaking for a New Territory: Enhancing GLM Pricing Model with a Bayesian Analysis. E-Forum, Journal of the Casualty Actuarial Society. Vol 2, pp.16--49.

[11]Miljkovic, T. and Sengupta, I. (2018), A New Analysis of VIX Index Using Mixture of Regressions: Examination and Short-Term Forecasting for S&P 500 Market. High Frequency. Vol. 1, Issue 1, pp.53--65.

[10] *Fang, L., *Wu, J., Miljkovic, T. (2017), Modeling Impact of Natural Hazard-Induced Disasters on Income Distribution in the United States. International Journal of Disaster Risk Science. Vol. 8, Issue 4, pp.435--444.

[9] Miljkovic, T. and Orr, M. (2017), An evaluation of the reconstructed coefficient of determination and potential adjustments. Communication is Statistics- Simulation and Computation. Vol. 46, Issue 9, pp.6705--6718.

[8] Miljkovic, T., Shaik, S., and Miljkovic, D. (2017), Redefining Standards for body mass index of the US population based on BRFSS data using mixtures. Journal of Applied Statistics. Vol. 44, Issue 2, pp.197--211.

[7] **Balic, R., Miljkovic, T., Bahri O., and Simsek, S. (2017), Utilization of Modified Wheat and Tapioca Starches as Fat Replacements in Bread Formulation. Journal of Food Processing and Preservation. Vol. 41, Issue 1. DOI:10.1111/jfpp.12888.

[6]Miljkovic, T. and Grün, B. (2016), Modeling loss data using mixtures of distributions. Insurance: Mathematics and Economics. Vol. 70, pp.387--396.

[5] **Galhenage, T., Hoffman, D., Silbert, S., Stafslien, S., Daniels, J., Miljkovic, T., Finlay, J., Franco, S., Clare, A., Nedved, B., Hadfield, M., Wendt, D., Waltz, G., Brewer, L., Teo, S., Lim, C.S., Webster, D. (2016), Fouling-release performance of silicone oil-modified siloxane-polyurethane coatings. Applied Materials & Interfaces. Vol. 8, Issue 42, pp.29025--29036.

[4] Miljkovic, T. and Barabanov, N. (2015), Modeling Veterans' Health Benefit Grants Using the EM Algorithm. Journal of Applied Statistics, Vol. 42, Issue 6, pp.1166--1182.

[3] Miljkovic, T. and Miljkovic, D. (2014), Modeling Impact of Hurricane Damages on Income Distribution in the Coastal U.S., International Journal of Disaster Risk Science, Vol. 5, Issue 4, pp.265--273.

[2] Maung, T.A., Gustafson, C.R., Saxowsky, D.M., Nowatzki, J.,  Miljkovic, T. and  Ripplinger, D. (2013), Logistics of Supplying Single vs. Multi-crop Cellulosic Feedstocks to a Biorefinery in Southeast North Dakota, Applied Energy, Vol. 109, Issue C, pp.229--238.

[1] Maung, T.A., Gustafson, C.R., Saxowsky, D.M., Miljkovic, T. and Nowatzki, J.F. (2012), Market Information on Sourcing Cellulosic Feedstock for Biofuel Production in Northern Plains Region of the United States, Journal of Agricultural Science and Technology, A2, pp.10--23.

Note: (*) undergraduate student. (**) graduate student

Education

  • Ph.D. in Statistics, North Dakota State University, Fargo, ND, USA, 2013
  • MBA. North Dakota State University, Fargo, ND, USA, 2007
  • M.S. Applied Mathematics (Actuarial Science), University of Illinois, Urbana-Champaign, IL, USA, 1996
  • B.S. Mechanical Engineering, University of Belgrade, Belgrade, Former Yugoslavia, 1991