Department of Mathematics and Statistics

Cheng Peng Ph.D.

Associate Professor and Coordinator of the Graduate Program in Statistics
Cheng Peng Ph.D.

Office

301-E Payson Smith, Portland Campus

Office Hours Spring 2015

Monday/Wednesday 10:00AM - 12:00PM

Contact Information

Phone: 207-780-4689

Courses in Spring 2015

MAT364  Numerical Analysis
MAT387/STA580  Bio/Applied Statistical Methods

Upcoming New Courses

MAT 486: Introduction to Big Data Analytics
MAT 488: Introduction to Data Mining
STA 586: Predictive Modeling with Big Data
STA 588: Statistical Data Mining

Courses Taught

Undergraduate Courses

MAT 105D: Mathematics for Quantitative Decision Making
MAT 108: College Algebra
MAT 120D: Introduction to Statistics
MAT 140: Pre-calculus
MAT 152D: Calculus A
MAT 220: Statistics for Biological Sciences
MAT 252: Calculus C
MAT 264: Statistical Computer Packages
MAT 281: Probability
MAT 282: Statistical Inference
MAT 295: Linear Algebra
MAT 364: Numerical Analysis
MAT 380: Probability and Statistics
MAT 386: Sampling Techniques
MAT 387: Introduction to Bio/Applied Statistics
MAT 485: Introduction to Applied Regression
MAT 487: Introduction to Categorical Data Analysis

Graduate Courses

STA 574: Statistical Computing
STA 579: Probability Models
STA 580: Statistical Inference
STA 580N: Bio/Applied Statistical Methods
STA 582 Introduction to Longitudinal Data Analysis
STA 583: Sampling Methods
STA 585: Regression Analysis
STA 585N: Regression and Forecasting
STA 587: Categorical Data Analysis
STA 588: Introduction to Biostatistics
STA 589: Survival Analysis

Research Interests

My primary research focuses on the applied statistical methodology and data analysis. I am particularly interested in statistical process control, reliability and survival modeling, statistical methods for environmental study, data visualization and statistical data mining. I am also interested in working with researchers in other disciplines on the applications of advanced statistical modeling.

Recent Publications

[1]. Xu, J and Peng, C. (2014). Fitting and Testing Two-sample Marshall-Olkin Extended Weibull Model with Randomly Censored Data, Journal of Applied Statistics, Vol. 41 Issue 12, pp 2577-2595. DOI:10.1080/02664763.2014.922166 (published online first, May 2014)

[2]. Gupta, R. C. and Peng, C. (2013). Proportional Odds Frailty Model and Stochastic Comparisons.  Annals of the Institute of Statistical Mathematics. Vol.66, pp 897–912. DOI 10.1007/s10463-013-0432-y (published online first in October, 2013)

[3]. Peng, C. and Xu, J. (2012). Nonparametric Confidence Limits of Quantile-based Process Capability Indices.  International journal of Quality, Statistics, and Reliability.  Volume 2012 (2012), Article ID 985152

[4]. Fisher, M. C. , Hochberg, M. C., El-Taha, M., Kremer, J. M.,  Peng, C.,  Greenberg, J. D. (2012).  Smoking, Smoking Cessation, and Disease Activity in a Large Cohort of Patients with Rheumatoid Arthritis. Journal of Rheumatology , 39(5):904-909. Epub 2012 Mar 15

[5]. Anandarajah,  A. P., El-Taha,  M,  Peng,  C,  Reed,  G, Greenberg,  J and Ritchlin, C. T. (2011). Association between focal erosions and generalised bone loss in psoriatic arthritis. Annals of the Rheumatic Disease, 70(7):1345-7. Epub 2011 Feb 16

[6]. Guan, Z. and Peng, C. (2011). Two-sample Semi-parametric (Reverse) Proportional Hazard Model. In JSM Proceedings, Section on Nonparametric Statistics. Alexandria, VA: American Statistical Association. 2067-2080.

[7]. Guan, Z. and Peng, C. (2011). A rank-based empirical likelihood approach to two-sample proportional odds model and its goodness-of-fit. Journal of Nonparametric Statistics.  Vol. 23(3), 763-780. DOI: 10.1080/10485252.2011. 559726

[8]. Gupta, R. C., Lvin, S. and Peng, C. (2010). Estimating Turning Points of Failure Rate of the Extended Weibull Distribution. Computational Statistics and Data Analysis. Vol. 54(4), 924-934.

[9]. Peng, C. (2010).  Interval estimation of population parameters based on environmental data with detection limits, Environmetrics. Vol. 21(6), 645-658.

[10]. Peng, C. (2010).   Parametric Lower Confidence Limits of Quantile-Based Process Capability Indices. Quality Technology & Quantitative Management, Vol. 7(3), 199-214.

[11]. Peng, C. (2010). Estimating and Testing Quantile-based Process Capability Indices for One-specification Interval and Skewed Distributions. Journal of Data Science, Vol. 8(2). 253 – 268.

[12]. Gupta, R. C. and Peng, C. (2009). Estimating reliability in Proportional Odds Ratio Models. Computational Statistics and Data Analysis. Vol 53(4),  1495-1510.

 

 Consulting Interests

I am interested in projects from almost all areas that involve significant statistical components. I usually work with collaborators / clients closely throughout the entire phase of consultation: from the initial stage of sampling and study design to modeling, outcome interpretation and the writing of formal and rigorous statistical report including both technical justifications and practical interpretations.

A partial list of models involved in numerous projects completed in last few years include linear regression models, binary logistic regression model, family of multi-category logit models, Poisson and loglinear regression models, lifetime regression models (non-parametric Kaplan-Meier, semi-parametric Cox proportional hazard and various parametric models including  accelerated failure time and competing risk models) based on both censored and completely observed data, time series modeling (exponential smoothing family of models, ARIMA and ARIMAX) and various random-effect regression models (including repeated measure ANOVA) for longitudinal data, models with high dimensional data (principal component analysis, factor analysis and structural equation modeling), linear and nonlinear discriminate analysis, cluster analysis, etc.

 

Programming / Computing

I have extensive experience in programming using SAS, R, SQL, LaTex and HTML/JavaScript/MATHJAX.  I also have good working knowledge of C, MATLAB, MATHEMATICA, SPSS and AMOS. MINITAB and Excel are regularly used in teaching introductory statistics.