Publications

  1. Brazauskas, V. and Serfling, R. (2000). “Robust and efficient estimation of the tail index of a single-parameter Pareto distribution” (with discussion). North American Actuarial Journal, 4(4), 12–27. Discussion: 5(3), 123–126. Reply: 5(3), 126–128.  PDF
  2. Brazauskas, V. and Serfling, R. (2000). “Robust estimation of tail parameters for two-parameter Pareto and exponential models via generalized quantile statistics”. Extremes, 3(3), 231–249.  PDF
  3. Brazauskas, V. (2000). “Robust parametric modeling of the proportional reinsurance premium when claims are approximately Pareto-distributed”. Proceedings of the American Statistical Association: Business and Economic Statistics Section, 144–149.  PDF
  4. Brazauskas, V. and Serfling, R. (2001). “Small sample performance of robust estimators of tail parameters for Pareto and exponential models”. Journal of Statistical Computation and Simulation, 70(1), 1–19.  PDF
  5. Brazauskas, V. (2002).”Fisher information matrix for the Feller-Pareto distribution”.
    Statistics and Probability Letters, 59(2), 159–167.  PDF
  6. Brazauskas, V. (2003). “Influence functions of empirical nonparametric estimators of net reinsurance premiums”. Insurance: Mathematics and Economics, 32(1), 115–133.  PDF
  7. Brazauskas, V. (2003). “Information matrix for Pareto (IV), Burr, and related distributions”. Communications in Statistics: Theory and Methods, 32(2), 315–325.  PDF
  8. Brazauskas, V. and Serfling, R. (2003). “Favorable estimators for fitting Pareto models: a study using goodness-of-fit measures with actual data”. ASTIN Bulletin, 33(2), 365–381.  PDF
  9. Brazauskas, V. and Kaiser, T. (2004). Discussion of “Empirical estimation of risk measures and related quantities” by Jones and Zitikis. North American Actuarial Journal, 8(3), 114–117.  PDF
  10. Brazauskas, V. (2004). “Nonparametric statistics”. In Encyclopedia of Actuarial Science (B. Sundt and J. Teugels, eds.), volume 2, 1182–1190; Wiley, London.  PDF
  11. Kaiser, T. and Brazauskas, V. (2006). “Interval estimation of actuarial risk measures”. North American Actuarial Journal, 10(4), 249–268.  PDF
  12. Brazauskas, V. and Ghorai, J. (2007). “Estimating the common parameter of normal models with known coefficients of variation: a sensitivity study of asymptotically efficient estimators”. Journal of Statistical Computation and Simulation, 77(8), 663–681.  PDF
  13. Dornheim, H. and Brazauskas, V. (2007). “Robust and efficient methods for credibility when claims are approximately gamma-distributed”. North American Actuarial Journal, 11(3), 138–158.  PDF
  14. Brazauskas, V., Jones, B., Puri, M., and Zitikis, R. (2007). “Nested L-statistics and their use in comparing the riskiness of portfolios”. Scandinavian Actuarial Journal, 107(3), 162–179.  PDF
  15. Brazauskas, V., Jones, B., and Zitikis, R. (2007). “Robustification and performance evaluation of empirical risk measures and other vector-valued estimators”. METRON — International Journal of Statistics, LXV(2), 175–199.  PDF
  16. Brazauskas, V., Jones, B., Puri, M., and Zitikis, R. (2008). “Estimating conditional tail expectations with actuarial applications in view”. Journal of Statistical Planning and Inference, 138(11), 3590–3604.  PDF
  17. Bajorunaite, R. and Brazauskas, V. (2008). “Method of trimmed moments for robust fitting of parametric failure time models”. METRON — International Journal of Statistics, LXVI(3), 341–360.  PDF
  18. Brazauskas, V., Jones, B., and Zitikis, R. (2009). “Robust fitting of claim severity distributions and the method of trimmed moments”. Journal of Statistical Planning and Inference, 139(6), 2028–2043.  PDF
  19. Brazauskas, V. (2009). “Robust and efficient fitting of loss models: diagnostic tools and insights”. North American Actuarial Journal, 13(3), 356–369.  PDF
  20. Brazauskas, V., Jones, B., and Zitikis, R. (2009). “When inflation causes no increase in claim amounts”. Journal of Probability and Statistics, volume 2009, 10 pages, doi:10 1155/2009/943926.  PDF
  21. Brazauskas, V. and Kleefeld, A. (2009). “Robust and efficient fitting of the generalized Pareto distribution with actuarial applications in view”. Insurance: Mathematics and Economics, 45(3), 424–435.  PDF
  22. Brazauskas, V. (2009). “Quantile estimation and the statistical relative efficiency curve”. METRON — International Journal of Statistics, LXVII(3), 289–301.  PDF
  23. Dornheim, H. and Brazauskas, V. (2011). “Robust-efficient fitting of mixed linear models: methodology and theory”. Journal of Statistical Planning and Inference, 141(4), 1422–1435.  PDF
  24. Dornheim, H. and Brazauskas, V. (2011). “Robust-efficient credibility models with heavy-tailed claims: a mixed linear models perspective”. Insurance: Mathematics and Economics, 48(1), 72–84.  PDF
  25. Brazauskas, V. and Kleefeld, A. (2011). “Folded- and log-folded-t distributions as models for insurance loss data”. Scandinavian Actuarial Journal, 2011(1), 59–74.   PDF
  26. Kleefeld, A. and Brazauskas, V. (2012). “A statistical application of the quantile mechanics approach: MTM estimators for the parameters of t and gamma distributions”. European Journal of Applied Mathematics, 23(5), 593–610.  PDF
  27. Brazauskas, V. and Kleefeld, A. (2014). Authors’ reply to “Letter to the Editor regarding folded models and the paper by Brazauskas and Kleefeld (2011)” by Scollnik. Scandinavian Actuarial Journal, 2014(8), 753–757.  PDF
  28. Brazauskas, V., Dornheim, H., and Ratnam, P. (2014). “Credibility and Regression Modeling”. In Predictive Modeling Applications in Actuarial Science, Volume I: Predictive Modeling Techniques (E. Frees, R. Derrig, G. Meyers, eds.), 217–235; Cambridge University Press.  PDF
  29. Dornheim, H. and Brazauskas, V. (2014). “Case studies using credibility and corrected adaptively truncated likelihood methods”. Variance, 7(2), 168–192.  PDF
  30. Brazauskas, V., Jones, B., and Zitikis, R. (2015). “Trends in disguise”. Annals of Actuarial Science, 9(1), 58–71.  PDF
  31. Brazauskas, V. and Kleefeld, A. (2016). “Modeling severity and measuring tail risk of Norwegian fire claims”. North American Actuarial Journal, 20(1), 1-16.  PDF
  32. Samanthi, R., Wei, W., and Brazauskas, V. (2016). “Ordering Gini indexes of multivariate elliptical risks”. Insurance: Mathematics and Economics, 68(3), 84–91.  PDF
  33. Yu, D. and Brazauskas, V. (2016). “Model uncertainty in operational risk modeling”. 2015 ERM Symposium, SOA Monograph, 1–21.  PDF
  34. Huang, S., Hartman, B., and Brazauskas, V. (2017). Model selection and averaging of health costs in episode treatment groups”. ASTIN Bulletin, 47(1), 153–167.  PDF
  35. Samanthi, R., Wei, W., and Brazauskas, V. (2017). “Comparing the riskiness of dependent portfolios via nested L-statistics”. Annals of Actuarial Science, 11(2), 237–252.  PDF
  36. Yu, D. and Brazauskas, V. (2017). “Model uncertainty in operational risk modeling due to data truncation: A single risk case”. Risks, 5(3), 17 pages.  PDF
  37. Kravtsov, S., Roebber, P., and Brazauskas, V. (2017). “A virtual climate library of surface temperature over North America for 1979–2015”. Scientific Data, 4(170155), 10 pages.  PDF
  38. Zhao, Q., Brazauskas, V., and Ghorai, J. (2018). “Robust and efficient fitting of severity models and the method of Winsorized moments”. ASTIN Bulletin, 48(1), 275–309.  PDF
  39. Zhao, Q., Brazauskas, V., and Ghorai, J. (2018). “Small-sample performance of the MTM and MWM estimators for the parameters of log-location-scale families”. Journal of Statistical Computation and Simulation, 88(4), 808–824.  PDF
  40. Brazauskas, V. and Upretee, S. (2019). “Model efficiency and uncertainty in quantile estimation of loss severity distributions”. Risks, 7(2), 16 pages.  PDF