MADUKA RUPASINGHE WEBSITE

Office: 215 Patterson
Phone: 419 207 6759
Email: mrupasin@ashland.edu
Office hours: MW 12:00 PM  1:00 PM TTH 1:30 PM  3:00 PM


Education

Research
My main research area is bootstrap methods in Time Series. I am also interested in Survival Analysis and Reliability Theory.
Publications

Rupasinghe M.; “Sieve Bootstrap Prediction Intervals for Time Series
with GARCH Errors,” submitted to Communications in Statistics – Simulation and
Computation

Weidenhamer J., Mohney BK., Shihada N., Rupasinghe M.; “Spatial and
Temporal Dynamics of Root Exudation: How important is Heterogeneity in
Allelopathic interaction?,” Journal of Chemical Ecology, 2014, 40 (8), 940952
 Rupasinghe M., Mukhopadhyay P., Samaranayake V.A.; “Obtaining
Predication Intervals for FARIMA processes using Sieve Bootstrap," Journal
of Statistical Computations and Simulations, 2014
Rupasinghe
M., Samaranayake V.A.; “The Asymptotic Properties of Sieve Bootstrap Prediction
Intervals for FARIMA processes,” Statistical and Probability Letters, 2012, 82 21082114 Gamagedara S., Kaczmarek A., Jiang,Y.,
Cheng X., Rupasinghe M.; “Validation Study of Urinary metabolites as Potential
Biomarkers for Prostate Cancer Detection,” Bioanalysis, 2012, 4, 1175118

Rupasinghe M., Samaranayake V.A.; “Prediction Interval for ARIMA
processes: A Sieve Bootstrap Approach,” 2012 Proceedings of the Joint Statistical Meetings, San Diego,
California Rupasinghe M., Mukhopadhyay P., Samaranayake V.A.; “Obtaining
Predication Intervals for FARIMA processes using Sieve Bootstrap," 2011Proceedings of the Joint Statistical Meetings, Miami,
Florida

Teaching
 FS14:MATH341 Applied Regression Analysis; MATH 201 Applied Calculus; MATH 208 Elementary Statistics; MATH 450 Math seminar
 SM14: MATH 201 Applied Calculus I; MATH 202 Applied Calculus II
 SP14: MATH 208 Elementary Statistics; MATH 318 Mathematical Statistics
 FS13: MATH 208 Elementary Statistics; MATH 317 Probability
 SP13: MATH 208 Elementary Statistics; MATH 202 Calculus II
 FS12: MATH 208 Elementary Statistics; MATH 341 Applied Intermediate Statistics
Teaching Tools developed by me
 If you want to be successful in a math class, you are encouraged go through this presentation.
 Understanding Law of Large Numbers with a simulation: an excel application.
 Understanding sampling distribution of sample mean (Central Limit Theorem) with a simulation: an excel application.
 Understanding confidence intervals with a simulation: an excel application.
 Understanding hypothesis testing with a simulation: an excel application.

Statistical Consulting
I offer a free statistical consulting service to the AU faculty and
students as a service to the campus community. The areas of specialization
include but are not limited to:
 Multiple Regression Analysis
 Time Series Analysis
 Principal Component Analysis
 Design of Experiments
 ANOVA / MANOVA
 Logistic, Poisson, Negative Binomial Regressions
 Factor Analysis
 Cluster Analysis
 Help with SPSS, MATLAB, R and other statistical softwares.

Useful Links
Here are some useful links:

