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. W. Li, L. Nghiem, F.K.C. Hui, A. H. Welsh (2024+). Sufficient dimension reduction in the presence of non-ignorable missing covariates, submitted.
  2. L. Nghiem and F.K.C. Hui (2024+). Random effects model-based sufficient dimension reduction for independent clustered data, accepted in Journal of American Statistical Association. (preprint).
  3. L. Nghiem, A. Ding, S. Wu (2023+). Statistical analyses for differentially-private matrix masking data, submitted.
  4. L. Nghiem and C. Potgieter (2023+). A linear errors-in-variables model with unknown heteroscedastic measurement errors, Statistica Sinica. Paper
  5. L. Nghiem,  F.K.C. Hui, S. Mueller, and A. H. Welsh (2024). Likelihood-based surrogate dimension reduction, Statistics and Computing. Paper
  6. 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. Paper
  7. 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. Paper
  8. 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. Paper
  9. L. Nghiem,  F.K.C. Hui, S. Mueller, and A. H. Welsh (2021). Sparse sliced inverse regression via Cholesky matrix penalization, Statistica Sinica. Paper
  10. M. Byrd, L. Nghiem, and M. Mcgee (2021). Bayesian regularization of Gaussian graphical models with measurement errors, Computational Statistics and Data Analysis. Paper
  11. L. Nghiem, M. Byrd, and C. Potgieter (2020). Estimation in linear errors-in-variables models with unknown error distribution, Biometrika. Paper
  12. L. Nghiem and C. Potgieter (2019). Simulation-Selection-Extrapolation: Estimation in high-dimensional errors-in-variables models, Biometrics. Paper
  13. L. Nghiem and C. Potgieter (2018). Phase function density deconvolution with heteroscedastic measurement error of unknown type (2018), Statistics in Medicine.  Paper

Applications

  1. Nghiem, L., Cao, J., Moon, C. Enhancing Empathic Accuracy: Penalized Functional Alignment Method to Correct Misalignment in Emotional Perception, submitted. Preprint
  2. Nghiem, L., Tabak, B., Wallmark, Z., Alvi, T., Cao, J. (2022). A Bayesian Latent Variable Model for Analysis of Empathic Accuracy, Recent Advances on Sampling Methods and Educational Statistics. Paper
  3. 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. Paper.
  4. Z. Wallmark, L. Nghiem, and L. Marks (2021). Does timbre modulate visual perception? Exploring cross-modal interactions, Music Perception: An Interdisciplinary Journal. Paper
  5. Z. Wallmark, R. Frank, and L. Nghiem (2019). Creating novel timbres from adjectives: An exploratory study using FM synthesis,  Psychomusicology: Music, Mind, and Brain. Paper
  6. L. Nghiem and T. Yunes (2016). A Heuristic Method for Scheduling Band Concert Tours, SIAM Journal of Undergraduate Research. Paper