• Jing, K., Khalili, A., Xu, C. (2024+) Class-specific Joint Feature Screening for Ultrahigh-dimensional Mixture Regressions. Journal of the American Statistical Association. To appear.

• Chavez Martinez*, G., Agarwal, A., Khalili, A., Ahmed, S. E. (2023). Penalized Estimation of Sparse Markov Regime-switching Vector Auto-regressive Models. Technometrics, https://doi.org/10.1080/00401706.2023.2201336.

• Khalili, A., Shokoohi, F, Asgharian, M, and Lin, S. (2023). Sparse Estimation in Semiparametric Finite Mixture of Varying Coefficient Regression Models, Biometrics, https://doi.org/10.1111/biom.13870.

•  Zhang*, D., Khalili, A., and Asgharian, M. (2022). Post-Model-Selection Inference in Linear Regression Models: An Integrated Review. Statistics Surveys, 16, 86-136.

•  Mojiri*, A., Khalili, A., and Hamadani, A. Z. (2022). New Hard-thresholding Rules based on Data Splitting in High-dimensional Imbalanced Classification. Electronic Journal of Statistics, 16, 814-861.

•  Manole*, T., Khalili, A. (2021). Estimating the Number of Components in Finite Mixture Models via the Group-Sort-Fuse Procedure. Annals of Statistics, 49, 3043–3069. 

(Winner of the Statistical Learning and Data Science ASA Student Paper Award, 2021)

•  McGillivray*, A., Khalili, A., and Stephens, D. (2020). Estimating sparse networks with hubs. Journal of Multivariate Analysis, 179, https://doi.org/10.1016/j.jmva.2020.104655.

•  Khalili, A. and Stephens, D. (2020). Sparseness, consistency and model selection for Markov regime-switching Gaussian autoregressive models. Statistica Sinica, 31, 1891-1914.

•  Shokoohi*, F., Khalili, A., Asgharian, M., and Lin, S. (2019). Capturing Heterogeneity of covariate Effects in Hidden Subpopulations in the Presence of Censoring and Large Number of Covariates. The Annals of Applied Statistics, 13, 444-465. 

•  Zhang*, F., Khalili, A. and Lin, S. (2019). Imprinting and Maternal Effect Detection Using Partial Likelihood Based on Discordant Sibpair Data. Statistica Sinica, 29, 1915-1937. 

•  Khalili, A. and Vidyashankar, A. N. (2018). Hypothesis Testing in Finite Mixture of Regressions: Sparsity and Model Selection Uncertainty. The Canadian Journal of Statistics, 46, 429-457. 

•  Khalili, A., Chen, J. and Stephens, D. (2017). Regularization and selection in Gaussian mixture of autoregressive models. The Canadian Journal of Statistics, 45, 356-374.

• Shohoudi*, A., Khalili, A., Wolfson, D., and Asgharian, M. (2016). Simultaneous Variable Selection and De-coarsening in Multi-path Change-point Models. Journal of Multivariate Analysis, 147, 202-217.

• Zhang*, F., Khalili, A. and Lin, S. (2015). Optimum Study Design for Detecting Imprinting and Maternal Effects Based on Partial Likelihood. Biometrics, 72, 95-105. 

•  Mcgillivray*, A., Khalili, A.(2014). A new penalized quasi-likelihood approach for estimating the number of states in a hidden Markov model. Contemporary Mathematics, Proceeding of the American Mathematical Society: Perspectives on Big Data Analysis: Methodologies and Applications, 622, 37-59.

•  Du*, Y., Khalili, A., Neslehova, J. G. and Steele, R. J. (2013). Simultaneous fixed and random effects selection in finite mixture of linear mixed-effects models. The Canadian Journal of Statistics, 41, 596-616.

• Khalili, A. and Lin, S. (2013). Regularization in finite mixture of regression models with diverging number of parameters. Biometrics, 69, 436-446.

• Khalili, A. (2011). An overview of the new feature selection methods in finite mixture of regression models. (Invited Review Paper). Journal of the Iranian Statistical Society, 10, 201-235.

• Khalili, A., Chen, J. and Lin, S. (2011). Feature selection in finite mixture of sparse normal linear models in high-dimensional feature space. Biostatistics, 12, 156-172.

• Khalili, A. (2010). New Estimation and Feature Selection Methods in Mixture-of-Experts Models. The Canadian Journal of Statistics, 38, 519-539.

• Garmaroudi, F., Marchant, D., Si, X., Khalili, A., et al. (2010). Pairwise network mechanisms in the host signaling response to coxsackievirus B3 infection. Proceedings of the National Academy of Sciences (PNAS), USA, 107, 17053-17058. 

•  Khalili, A., Huang, T. and Lin, S. (2009) A Robust Unified Approach to Methylation and Gene Expression Profiling through Flexible Modeling of Variation. Journal of Computational Statistics and Data Anlaysis, 53, 1701-1710.

• Chen, J. and Khalili, A. (2008). Order Selection in Finite Mixture Models with a Non-smooth Penalty. Journal of the American Statistical Association, 103, 1674-1683.

• Khalili, A. and Chen, J. (2007). Variable Selection in Finite Mixture of Regression Models. Journal of the American Statistical Association, 102, 1025-1038.

• Khalili, A., Potter, D., Yan, P., Li, L., Gray, J., Huang, T., and Lin, S. (2007). Gamma-Normal-Gamma Mixture Model for Detecting Differentially Methylated Loci in Three Breast Cancer Cell Lines. Cancer Informatics, 2, 43-54.

• Khalili, A., Yang, A., and Da, X. (2022-2023). Estimation and Group-feature Selection in Sparse Mixture-of-Experts Models with Diverging Number of Parameters. (Submitted, Under Revision)

•  Rejali, M., Medini, S., Hamou-Lhadj, A., and Khalili, A. (2018). Automatic Segmentation of Execution Traces. (Technical report)

•  Khalili, A., Chen, J., and Stephens, D. (2016). Chapter 2: Regularization in regime-switching Gaussian autoregressive models. Advanced Statistical Methods in Data Science: International Chinese Statistical Association (ICSA) Book Series in Statistics, 13-32. Published, Springer Singapore. 

• Khalili, A. (2006). Order Selection in Classical Finite Mixture Models, and Variable Selection and Inference in Finite Mixture of Regression Models, PhD Thesis, Department of Statistics and Actuarial Sciences, University of Waterloo, Canada.