Biostatistics and Data Science

RESEARCH INTEREST

Biochemical reaction networks, stochastic epidemic models, Bayesian variable selection, efficient sampling design

Dr. Linder’s research interests lie broadly in developing mathematical and statistical methods to address the plethora of new data types encountered in applied research. Specifically, he has developed methods for parameter inference and topology estimation in stochastic biological systems, with emphasis on gene regulatory networks (microarray and RNA-seq analysis) and disease dynamic models. He is particularly interested in the interplay of stochastic biological models and Bayesian statistics. In addition, he has developed sampling methodology to improve sample information, and hence increase power, in linear and generalized linear models, which will allow researchers to decrease study costs while maintaining desired study power.

TEACHING AREAS

Graduate teachings in Bayesian statistics, stochastic processes, biostatistics, SAS/R data management

SELECTED PUBLICATIONS

• Yelena N. Tarasenko, Daniel F. Linder, Eric E. Miller, Physical activity and mortality among 3+ year cancer survivors in the U.S., Cancer Causes and Control (2018) doi: 10.1007/s10552-018-1017-0
• Hani Samawi, Jingxian Cai, Daniel F. Linder, Haresh Rochani, Jingjing Yin, A simpler approach for mediation analysis for dichotomous mediators in logistic regression, Journal of Statistical Computation and Simulation (2018) doi: 10.1080/00949655.2018.1426762
• Hani Samawi, Haresh D. Rochani, Jingjing Yin, Daniel F. Linder, Robert L. Vogel, Notes on kernel based mode estimation using more efficient sampling designs, Computational Statistics (2018) doi: 10.1007/s00180-017-0787-2
• Daniel F. Linder, Jingjing Yin, Haresh Rochani, Hani Samawi, Sanjay Sethi, Increased Fisher’s information for parameters of association in count regression via extreme ranks, Communications in Statistics (Theory and Methods) (2017) doi: 10.1080/03610926.2017.1316859
• Daniel F. Linder, Viral Panchal, Hani Samawi, Duchwan Ryu, Balanced Bayesian LASSO for Heavy Tails, Journal of Statistical Computation and Simulation (2016), 86(6):1115-1132
• Shusen Pu, Broderick O. Oluyede, Yuqi Qiu, and Daniel F. Linder, A generalized class of exponentiated modified Weibull distribution with applications, Journal of Data Science (2016), 14(4):585-614
• Jingjing Yin, Hani Samawi, and Daniel F. Linder, Improved non-parametric estimation of the optimal diagnostic cut-off point associated with the Youden index under different sampling schemes, Biometrical Journal (2016) doi: 10.1002/bimj.201500036
• Haresh Rochani, Robert Vogel, Hani Samawi, Daniel F. Linder, Estimates for cell counts and common odds ratio in three-way contingency tables by homogeneous log-linear models with missing data, Advances in Statistical Analysis (2016) doi: 10.1007/s10182-016-0275-y 

• Daniel F. Linder, Grzegorz Rempala, Bootstrapping Least Squares Estimates for Biochemical Reaction Networks, Journal of Biological Dynamics (2015) 9, 125-146 doi: 10.1080/17513758.2015.1033022
• Daniel F. Linder, Hani Samawi, Lili Yu, Arpita Chatterjee, Yisong Huang, Robert Vogel, On Stratified Bivariate Ranked Set Sampling for Regression Estimators, Journal of Applied Statistics (2015) doi: 10.1080/02664763.2015.1043868
• Daniel F. Linder, Grzegorz Rempala, Algebraic Statistical Model for Biochemical Dynamics Inference, Journal of Coupled Systems and Multiscale Dynamics 1, 468-475 (2013)