Ph.D., University of Cincinnati, 2013
Hume Hall 308
(662) 915-7436 | email@example.com
TEACHING, Spring 2020
Probability Theory (stochastic processes, Markov and reversible Markov chains, central limit theorems, dependence coefficients and applications), Statistics (kernel estimation of dependent data, Bayesian analysis, survival analysis, methods of estimation, modelling and testing with Markov chains, large sample theory)
- M. Longla: Remarks on limit theorems for reversible Markov chains and their applications, Journal of Statistical Planning and Inference (2017), pp. 28-43.
- M. Longla, M. Peligrad, H. Sang, On kernel estimators of density for reversible Markov chains, Statistics and Probability Letters 100 (2015), 149-157.
- M. Longla, On mixtures of copulas and mixing coefficients, Journal of Multivariate Analysis 139 (2015), 259-265.
- M. Longla, S. Sivaganesan, R. Zhou, An objective bayesian estimation of parameters in a log-binomial model, J. Statist. Plann. Inference (2014) pp. 113-121.
- M. Longla, Remarks on the speed of convergence of mixing coefficients and applications, Statist. Probab. Lett. 83 (2013), 2439-2445.
- M. Longla, On dependence structure of copula-based Markov chains, ESAIM: Probability and Statistics, doi:10.1051/ps/2013052.
- M. Longla, M. Peligrad, Some aspects of modeling dependence in copula-based Markov chains, J. Multivariate Anal. 111 (2012), 234–240.
- M. Longla, C. Peligrad, M. Peligrad, On the functional central limit theorem for reversible Markov chains with nonlinear growth of the variance, J. Appl. Probab. 49 (2012), no. 4, 1091–1105.