- Landau, W., Niemi, J., and Nettleton, D., “Fully Bayesian analysis of RNA-seq counts for the detection of gene expression heterosis”. Accepted into the Journal of the American Statistical Association on April 28, 2018.**
- Landau, W. (2018), “The drake R package: a pipeline toolkit for reproducibility and high-performance computing”. Journal of Open Source Software, 3(21), 550, https://doi.org/10.21105/joss.00550
- Niemi, J., Mittman, E., Landau, W., and Nettleton, D. (2015), “Empirical Bayes Analysis of RNA-seq Data for Detection of Gene Expression Heterosis,” Journal of Agricultural, Biological, and Environmental Statistics, 20, 1-15. Available at link.springer.com.
- Landau, W. and Liu, P. (2013), “Dispersion Estimation and Its Effect on Test Performance in RNA-seq Data Analysis: A Simulation-Based Comparison of Methods,” PLOS One, 8. Available at journals.plos.org.
- Ratliff, B., Womack. C., Tang, X., Landau, W., Butler, L., and Szpunar, D. (2010), “Modeling the Rovibrationally Excited C2H4OH Radicals from the Photodissociation of 2-Bromoethanol at 193 nm,” Journal of Physical Chemistry, 114, 4934-4945. Available at ncbi.nlm.nih.gov.
Articles under review
- Landau, W. and Niemi, J., “A fully Bayesian strategy for high-dimensional hierarchical modeling using massively parallel computing,” submitted to the Journal of Computational and Graphical Statistics on March 8, 2016. Preprint available at arxiv.org.