Dr. Ye Liang
Submitted by gina on Mon, 2015-01-05 15:02
EPSCoR Research Focus:
Socio-ecological Modeling and Prediction System
Assistant Professor
Department of Statistics
Oklahoma State University
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Education:
B.S. | Mathematics | Nanjing University, China | 2006
M.A. | Statistics | University of Missouri, Columbia, MO | 2009
Ph.D. | Statistics | University of Missouri, Columbia, MO | 2012
Research Interests:
Dr. Ye Liang’s general research interests include:
- Bayesian statistics, Bayesian hierarchical models, Bayesian computations
- Spatial statistics, spatio-temporal models, dynamic state-space models
- Gaussian graphical models, Markov random fields, lattice data
- Survival analysis, reliability analysis, lifetime data
Dr. Ye Liang’s research focus in the Oklahoma EPSCoR project is integrated statistical modeling and analysis for social-ecological data. Currently he is particularly involved in modeling ecological processes such as soil moisture, precipitation and streamflow.
Difficulties are twofold: First, most processes are nested with each other and exact physical relationships are unclear; Second, data used for analysis are from various observational sources and are highly variable. Dr. Ye Liang’s main objective in this project is to apply Bayesian methods, along with other cutting-edge statistical tools, to integrate these processes and data in a theoretically justified modeling framework.
Research Assistants Funded by EPSCoR:
Xijia Han (Graduate Student)
Dept. of Statistics, Oklahoma State University
Research Focus: Modeling soil moisture in Oklahoma using Mesonet data by building a spatial-temporal model and applying Bayesian technique.
Email: henry.han@okstate.edu
Key Publications:
- Hendershot M and Liang Y. Quantifying legal landmarks: applying the legislative ac- complishment approach to the decisions of the Supreme Court. In revision.
Access the Publication - Liang Y. A graph-based multivariate conditional autoregressive model. In revision.
Access the Publication - Liang Y and Sun D (2014). Identifiability of masking probabilities in the competing risks model with emphasis on Weibull models. Communications in Statistics - Theory and Methods. In press.
- Liang Y, Sun D, He Z and Schootman M (2014). Modeling bounded outcome scores using the binomial-logit-normal distribution. Chilean Journal of Statistics, Vol. 5-2, 3-14.
- Liang Y and Sun D (2012). Objective priors for generative star-shape models, Statistics & Probability Letters, Vol. 82, 991-997.
Curriculum Vitae:
