@article{zheng_tang_huang_wu_2022, title={An O(N) algorithm for computing expectation of N-dimensional truncated multi-variate normal distribution II: computing moments and sparse grid acceleration}, volume={48}, ISSN={["1572-9044"]}, DOI={10.1007/s10444-022-09988-6}, number={6}, journal={ADVANCES IN COMPUTATIONAL MATHEMATICS}, author={Zheng, Chaowen and Tang, Zhuochao and Huang, Jingfang and Wu, Yichao}, year={2022}, month={Dec} }
 @article{zheng_wu_2020, title={Nonparametric Estimation of Multivariate Mixtures}, volume={115}, ISSN={["1537-274X"]}, DOI={10.1080/01621459.2019.1635481}, abstractNote={A multivariate mixture model is determined by three elements: the number of components, the mixing proportions, and the component distributions. Assuming that the number of components is given and ...}, number={531}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Zheng, Chaowen and Wu, Yichao}, year={2020}, month={Jul}, pages={1456–1471} }
 @article{zheng_wu_2020, title={Tuning parameter selection for penalised empirical likelihood with a diverging number of parameters}, volume={32}, ISSN={["1029-0311"]}, DOI={10.1080/10485252.2020.1717491}, abstractNote={ABSTRACT Penalised likelihood methods have been a success in analysing high dimensional data. Tang and Leng [(2010), ‘Penalized High-Dimensional Empirical Likelihood’, Biometrika, 97(4), 905–920] extended the penalisation approach to the empirical likelihood scenario and showed that the penalised empirical likelihood estimator could identify the true predictors consistently in the linear regression models. However, this desired selection consistency property of the penalised empirical likelihood method relies heavily on the choice of the tuning parameter. In this work, we propose a tuning parameter selection procedure for penalised empirical likelihood to guarantee that this selection consistency can be achieved. Specifically, we propose a generalised information criterion (GIC) for the penalised empirical likelihood in the linear regression case. We show that the tuning parameter selected by the GIC yields the true model consistently even when the number of predictors diverges to infinity with the sample size. We demonstrate the performance of our procedure by numerical simulations and a real data analysis.}, number={1}, journal={JOURNAL OF NONPARAMETRIC STATISTICS}, author={Zheng, Chaowen and Wu, Yichao}, year={2020}, month={Jan}, pages={246–261} }
 @article{oh_foster_williams_zheng_ru_lunn_mowat_2019, title={Diagnostic utility of clinical and laboratory test parameters for differentiating between sudden acquired retinal degeneration syndrome and pituitary‐dependent hyperadrenocorticism in dogs}, volume={22}, ISSN={1463-5216 1463-5224}, url={http://dx.doi.org/10.1111/vop.12661}, DOI={10.1111/vop.12661}, abstractNote={Abstract}, number={6}, journal={Veterinary Ophthalmology}, publisher={Wiley}, author={Oh, Annie and Foster, Melanie L. and Williams, Jonathan G. and Zheng, Chaowen and Ru, Hongyu and Lunn, Katharine F. and Mowat, Freya M.}, year={2019}, month={Mar}, pages={842–858} }
 @article{young_zheng_davidson_westermeyer_2019, title={Visual outcome in cats with hypertensive chorioretinopathy}, volume={22}, ISSN={["1463-5224"]}, url={https://doi.org/10.1111/vop.12575}, DOI={10.1111/vop.12575}, abstractNote={Abstract}, number={2}, journal={VETERINARY OPHTHALMOLOGY}, author={Young, Whitney M. and Zheng, Chaowen and Davidson, Michael G. and Westermeyer, Hans D.}, year={2019}, month={Mar}, pages={161–167} }