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Cooray, Kahadawala

Professor

FACULTY

More about Kahadawala Cooray

  • Cooray, K. (2018). Strictly Archimedean copulas with complete association for multivariate dependence based on the Clayton family. Dependence Modeling, 6(1), 1-18.
  • Cooray, K. (2018). A new extension of the FGM copula for negative association. Communications in Statistics—Theory and Methodshttps://doi.org/10.1080/03610926.2018.1440312
  • Mdziniso, N. C. and Cooray, K. (2018). Odd Pareto families of distributions for modeling loss payment data. Scandinavian Actuarial Journal, 2018(1), 42-63.
  • Mdziniso, N. C. and Cooray, K. (2018). Parametric analysis of renal failure data using the exponentiated Odd Weibull distribution. International Journal of Statistics in Medical Research, 7(3), 96-105.
  • Cooray, K. (2015). A study of moments and likelihood estimators of the odd Weibull distribution. Statistical Methodology, 26, 72-83.
  • Cooray, K., Cheng, C.-I. (2015). Bayesian estimators of the lognormal-Pareto composite distribution. Scandinavian Actuarial Journal, 2015(6), 500-515.
  • Cooray, K. (2013). Exponentiated sinh Cauchy distribution with applications. Communications in Statistics—Theory and Methods, 42(21), 3838-3852.
  • Cooray, K. (2012). Analyzing grouped, censored and truncated data using the odd Weibull family. Communications in Statistics—Theory and Methods, 41(15), 2661-2680.
  • Cooray, K. (2010). Generalized Gumbel distribution. Journal of Applied Statistics, 37(1), 171-179.
  • Cooray, K., Gunasekera, S., and Ananda, M. M. A. (2010). Weibull and inverse Weibull composite distribution for modeling reliability data. Model Assisted Statistics and Applications, 5(2), 109-115.
  • Cooray, K. (2009). The Weibull-Pareto composite family with applications to the analysis of unimodal failure rate data. Communications in Statistics—Theory and Methods, 38(11), 1901-1915.
  • Cooray, K., Ananda, M. M. A. (2008). A generalization of the half-normal distribution with applications to lifetime data. Communications in Statistics—Theory and Methods, 37(9), 1323–1337.
  • Cooray, K. (2006). Generalization of the Weibull distribution: the odd Weibull family. Statistical Modelling, 6(3), 265-277.
  • Cooray, K., Gunasekera, S., Ananda, M. M. A. (2006). The folded logistic distribution. Communications in Statistics—Theory and Methods, 35(3), 385-393. 
  • Cooray, K., Ananda, M. M. A. (2005). Modeling actuarial data with a composite lognormal-Pareto model. Scandinavian Actuarial Journal, 2005(5), 321-334.

Selected Presentations

  • Cooray, K. (August 2011). Exponentiated sinh Cauchy distribution with applications. Joint Statistical Meeting (JSM), American Statistical Association, Miami Beach, Florida.
  • Cooray, K. (August 2010). The odd Weibull family for modeling incomplete data. Joint Statistical Meeting (JSM), American Statistical Association, Vancouver, Canada.
  • Ananda, M. M. A., Gunasekera, S., Cooray, K. (August 2008). Folded parametric families. Joint Statistical Meeting (JSM), American Statistical Association, Denver, Colorado.
  • Ananda, M. M. A., Cooray, K. (August 2003). An alternative distribution to Weibull distribution that was overlooked in the literature. Joint Statistical Meeting (JSM), American Statistical Association, San Francisco.
  • Cooray, K., Ananda, M. M. A. (August 2003). Modeling insurance data with a composite lognormal-Pareto model. Joint Statistical Meeting (JSM), American Statistical Association, San Francisco.​

Honors and awards

  • First Ph.D. graduate in mathematical sciences in the state of Nevada. Department of Mathematical Sciences, University of Nevada at Las Vegas, Summer 2008
  • Wolzinger Family Research Scholarship - Science - Grad award. University of Nevada at Las Vegas, Spring 2008
  • Ph.D., University of Nevada at Las Vegas, 2008
  • B.Sc., University of Colombo, Sri Lanka, 1994
  • Modeling continuous statistical distributions and inferences, with applications to actuarial and medical sciences
  • American Statistical Association
  • Mid-Michigan ASA Chapter

Courses Taught

  • Statistics
  • Actuarial Science