Research

My general research interests are statistical methods for nonparametric and high dimensional settings. My methodological research is focused on measurement error modeling, graphical model, and sufficient dimension reduction. In addition, I collaborated with scientists in various fields to address scientific questions using data.  

PUBLICATIONS AND PREPRINTS

Statistical Theory and Methodology

  1. L. Nghiem and F.K.C. Hui (2023+). Random effect sufficient dimension reduction for clustered data, submitted (preprint).
  2. L. Nghiem, A. Ding, S. Wu (2023+). Statistical analyses for differentially-private matrix masking data, submitted.
  3. L. Nghiem and C. Potgieter (2023+). A linear errors-in-variables model with unknown heteroscedastic measurement errors, to appear in Statistica Sinica. (arXiv link)
  4. L. Nghiem,  F.K.C. Hui, S. Mueller, and A. H. Welsh (2023+). Likelihood-based surrogate dimension reduction, to appear in Statistics and Computing. (arXiv link)
  5. L. Nghiem,  F.K.C. Hui, S. Mueller, and A. H. Welsh (2022). Screening methods for linear errors-in-variables models in high dimensions. Biometrics. https://doi.org/10.1111/biom.13628.
  6. L. Nghiem,  F.K.C. Hui, S. Mueller, and A. H. Welsh (2022). Estimation of graphical models for skew continuous data. Scandinavian Journal of Statistics. https://doi.org/10.1111/sjos.12569
  7. F.K.C. Hui and L. Nghiem (2022). Sufficient dimension reduction for clustered data via finite mixture modelling, Australian and New Zealand Journal of Statistics. https://doi.org/10.1111/anzs.12349
  8. L. Nghiem,  F.K.C. Hui, S. Mueller, and A. H. Welsh (2021). Sparse sliced inverse regression via Cholesky matrix penalization, Statistica Sinica. doi:10.5705/ss.202020.0406
  9. M. Byrd, L. Nghiem, and M. Mcgee (2021). Bayesian regularization of Gaussian graphical models with measurement errors, Computational Statistics and Data Analysis 156, 107085. (arXiv link)
  10. L. Nghiem, M. Byrd, and C. Potgieter (2020). Estimation in linear errors-in-variables models with unknown error distribution, Biometrika 107(4), 841-856. (arXiv link)
  11. L. Nghiem and C. Potgieter (2019). Simulation-Selection-Extrapolation: Estimation in high-dimensional errors-in-variables models. Biometrics 75(4), 1133-1144. Paper Supplemental
  12. L. Nghiem and C. Potgieter (2018). Phase function density deconvolution with heteroscedastic measurement error of unknown type (2018), Statistics in Medicine 37(25), 3679-3692.  pdf

Applications

  1. Nghiem, L., Tabak, B., Wallmark, Z., Alvi, T., Cao, J. (2022). A Bayesian Latent Variable Model for Analysis of Empathic Accuracy. In: Ng, H.K.T., Heitjan, D.F. (eds) Recent Advances on Sampling Methods and Educational Statistics. Emerging Topics in Statistics and Biostatistics. Springer, Cham. https://doi.org/10.1007/978-3-031-14525-4_10
  2. Tabak, B. A, Wallmark, Z., Nghiem, L., Alvi, T., Sunahara, C. S., Lee, J, & Cao, J. (2022). Initial evidence for a relation between behaviorally assessed empathic accuracy and affect sharing for people and music, Emotion, doi: 10.1037/emo0001094.
  3. Z. Wallmark, L. Nghiem, and L. Marks (2021). Does timbre modulate visual perception? Exploring cross-modal interactions. Music Perception: An Interdisciplinary Journal 39 (1): 1–20.
  4. Z. Wallmark, R. Frank, and L. Nghiem (2019). Creating novel timbres from adjectives: An exploratory study using FM synthesis,  Psychomusicology: Music, Mind, and Brain 29(4), 188. pdf
  5. L. Nghiem and T. Yunes (2016). A Heuristic Method for Scheduling Band Concert Tours, SIAM Journal of Undergraduate Research 9. pdf