@article{martin_han_2016, title={A semiparametric scale-mixture regression model and predictive recursion maximum likelihood}, volume={94}, ISSN={0167-9473}, url={http://dx.doi.org/10.1016/J.CSDA.2015.08.005}, DOI={10.1016/j.csda.2015.08.005}, abstractNote={To avoid specification of a particular distribution for the error in a regression model, we propose a flexible scale mixture model with a nonparametric mixing distribution. This model contains, among other things, the familiar normal and Student-t models as special cases. For fitting such mixtures, the predictive recursion method is a simple and computationally efficient alternative to existing methods. We define a predictive recursion-based marginal likelihood function, and estimation of the regression parameters proceeds by maximizing this function. A hybrid predictive recursion–EM algorithm is proposed for this purpose. The method’s performance is compared with that of existing methods in simulations and real data analyses.}, journal={Computational Statistics & Data Analysis}, publisher={Elsevier BV}, author={Martin, Ryan and Han, Zhen}, year={2016}, month={Feb}, pages={75–85} }
 @article{fancher_han_levin_page_reich_smith_wilson_jones_2016, title={Use of Bayesian inference in crystallographic structure refinement via full diffraction profile analysis}, volume={6}, journal={Scientific Reports}, author={Fancher, C. M. and Han, Z. and Levin, I. and Page, K. and Reich, B. J. and Smith, R. C. and Wilson, A. G. and Jones, J. L.}, year={2016} }