arrow Publications :
  • Chavez Martinez*, G., Agarwal, A., Khalili, A., Ahmed, S. E. (2023). Penalized Estimation of Sparse Markov Regime-switching Vector Auto-regressive Models. (Accepted by Technometrics).
    Technical Report

  • Khalili, A., Shokoohi, F, Asgharian, M., and Lin, S. (2023). Sparse Estimation in Semi-parametric Finite Mixture of Varying Coefficient Regression Models. (Accepted by Biometrics).
    Technical Report

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

  • Mojiri*, A., Khalili, A., and Hamadani, A. Z. (2021). New Hard-thresholding Rules based on Data Splitting in High-dimensional Imbalanced Classification. (Accepted by Electronic Journal of Statistics).
    Published version

  • Manole*, T., Khalili, A. (2021). Estimating the Number of Components in Finite Mixture Models via the Group-Sort-Fuse Procedure. (Accepted by Annals of Statistics).
    • (Winner of the Statistical Learning and Data Science ASA Student Paper Award, 2021)
    arXiv version

  • McGillivray*, A., Khalili, A., and Stephens, D. (2020). Estimating sparse networks with hubs. (Accepted by Journal of Multivariate Analysis).
    • Accepted version

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

  • 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.
    • Accepted version

  • 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.
    • Accepted version

  • 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.
    • Accepted version

  • 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. Accepted version

  • 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. Accepted version

  • 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. (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.



arrow Other Publications :
  • 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.



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