@article{ma_xiao_liu_lindquist_2021, title={A functional mixed model for scalar on function regression with application to a functional MRI study}, volume={22}, ISSN={["1468-4357"]}, DOI={10.1093/biostatistics/kxz046}, abstractNote={Summary}, number={3}, journal={BIOSTATISTICS}, publisher={Oxford University Press (OUP)}, author={Ma, Wanying and Xiao, Luo and Liu, Bowen and Lindquist, Martin A.}, year={2021}, month={Jul}, pages={439–454} }
 @article{liu_ghosh_2020, title={On empirical estimation of mode based on weakly dependent samples}, volume={152}, ISSN={["1872-7352"]}, url={https://doi.org/10.1016/j.csda.2020.107046}, DOI={10.1016/j.csda.2020.107046}, abstractNote={Given a large sample of observations from an unknown univariate continuous distribution, it is often of interest to empirically estimate the global mode of the underlying density. Applications include samples obtained by Monte Carlo methods with independent observations, or Markov Chain Monte Carlo methods with weakly dependent samples from the underlying stationary density. In either case, often the generating density is not available in closed form and only empirical determination of the mode is possible. Assuming that the generating density has a unique global mode, a non-parametric estimate of the density is proposed based on a sequence of mixtures of Beta densities which allows for the estimation of the mode even when the mode is possibly located on the boundary of the support of the density. Furthermore, the estimated mode is shown to be strongly universally consistent under a set of mild regularity conditions. The proposed method is compared with other empirical estimates of the mode based on popular kernel density estimates. Numerical results based on extensive simulation studies show benefits of the proposed methods in terms of empirical bias, standard errors and computation time. An R package implementing the method is also made available online.}, journal={COMPUTATIONAL STATISTICS & DATA ANALYSIS}, publisher={Elsevier BV}, author={Liu, Bowen and Ghosh, Sujit K.}, year={2020}, month={Dec} }