Dobrin A. Marchev

Journal Articles

(2009). New and Improved Estimation of Distribution Functions under Second Order Stochastic Ordering. Journal of Nonparametric Statistics, 21(2). 143-153.

(2007). A Theoretical Comparison of the Data Augmentation, Marginal Augmentation and PX-DA Algorithms. The Annals of Statistics, 36.

(2004). Geometric Ergodicity of van Dyk and Meng’s Algorithm for the Multivariate Student’s t model. Journal of the American Statistical Association, (99). 228-238.

(2004). Stability of the Tail Markov Chain and the Evaluation of Improper Priors for an Exponential Rate Parameter. Bernoulli,, (10). 549–564.

Presentations

Marchev, D. (2007, June 25). A Theoretical Comparison of the Data Augmentation, Marginal Augmentation and PX-DA Algorithms. New Directions in Monte Carlo Methods Workshop. Fleurance, France

Marchev, D. (2006, March 8). Bootstrap Methods. Department of Statistics, and Computer Information Systems. NY: Baruch College CUNY.

Marchev, D. (2004, March 9). Monte Carlo Methods for Posterior Distributions Associated with Multivariate Student’s t Data. Department of Mathematics and Statistics. Auburn, AL: Auburn University.

Marchev, D. (2004, March 3). Monte Carlo Methods for Posterior Distributions Associated with Multivariate Student’s t Data. Department of Mathematics. New Orleans, LA: Tulane University.

Marchev, D. (2004, February 26). Monte Carlo Methods for Posterior Distributions Associated with Multivariate Student’s t Data. Department of Statistics. Columbia, SC: University of South Carolina.

Marchev, D. (2004, March 31). Monte Carlo Methods for Posterior Distributions Associated with Multivariate Student’s t Data. Department of Mathematics. San Juan, Puerto Rico: University of Puerto Rico.

Marchev, D. (2004, April 5). Monte Carlo Methods for Posterior Distributions Associated with Multivariate Student’s t Data. Department of Statistics. Coventry, United Kingdom: University of Warwick.

Marchev, D. (2004, February 18). Monte Carlo Methods for Posterior Distributions Associated with Multivariate Student’s t Data. Department of Statistics and Computer Information Systems. New York, NY: Baruch College.

Marchev, D. (2004, January 27). Monte Carlo Methods for Posterior Distributions Associated with Multivariate Student’s t Data. Department of Mathematics and Statistics. Baltimore, MD: University of Maryland Baltimore County.

Marchev, D. (2004, February 3). Monte Carlo Methods for Posterior Distributions Associated with Multivariate Student’s t Data. School of Operations Research and Industrial Engineering. Ithaca, NY: Cornell University.

Marchev, D. (2003, November 20). Monte Carlo Methods for Posterior Distributions Associated with Multivariate Student’s t Data. Department of Statistics,. Gainesville, FL: University of Florida.

Marchev, D. (2002, September 28). Analysis of van Dyk and Meng’s Data Augmentation Algorithm for the Multivariate t Model. Challenges in Stochastic Computation Workshop. Research Triangle Park, NC: Statistical and Applied Mathematical Sciences Institute (SAMSI).

Ignatov, T. Z., Marchev, D., & Madjarova, T. (1999, December 31). "Absorption of the Poison Process Between Two Boundaries" pp. 113-119. Proceedings for the Twenty Eighth Spring Conference of the Union of Bulgarian Mathematicians.

Marchev, D. (1999, April 6). Absorption of the Poisson Process Between Two Boundaries. 28th Spring Conference of the Union of Bulgarian Mathematicians. Montana, Bulgaria

ignatov, T. Z., Marchev, D., & Liubenova, M. (1998, December 31). "About the Ruin Probability in the Classical Poison Model" pp. 155-160. Proceedings of the Twenty Seventh Spring Conference of the Union of Bulgarian Mathemtaicians.

Marchev, D. (1998, November 19). The Ruin Probability in the Classical Poisson Model. Conference on The Transition Processes in the Bulgarian Economy. Sofia University, Sofia, Bulgaria: Department of Economics and Business Administration.

Research Currently in Progess

Marchev, D.(n.d.). Estimating the Maximal Correlation Coefficient. In Progress.

Marchev, D., & Johnson, M.(n.d.). Marginal Empirical Likelihood Inference for the Mean of a Population. In Progress.