Under review

Dallakyan A. and Pourahmadi M. (2024+). An Alternative Graphical Lasso Algorithm for Precision Matrices. Available on the arxiv.

Dallakyan A. and Pourahmadi M. (2024+). Learning Bayesian Networks through Birkhoff Polytope: A Relaxation Method. Available on the arxiv. Python Code

Peer-reviewed

Dallakyan A. and Yang Ni (2025). Generalized Criterion for Identifiability of Additive Noise Models Using Majorization. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, Available on the arxiv.

Dallakyan A. (2024). On Learning Time Series Summary DAGs: A Frequency Domain Approach, Econometrics and Statistics, Available here. [Python Code: Available Soon]

Dallakyan, A. & Pourahmadi, M. (2023) Fused-Lasso Regularized Cholesky Factors of Large Nonstationary Covariance Matrices of Replicated Time Series, Journal of Computational and Graphical Statistics, Available here. R Script

Dallakyan, A., Kim, R., & Pourahmadi, M. (2022). Time series graphical lasso and sparse VAR estimation. Computational Statistics & Data Analysis, 176, 107557. Available here. R Package

Dallakyan, A. (2022). graphiclasso: Graphical lasso for learning sparse inverse-covariance matrices. The Stata Journal. Available here. Stata package

Dallakyan A. (2020). Nonparanormal Structural VAR for Non-Gaussian Data. Journal of Comp. Economics. Available here

Bakhtavoryan R., Capps O. , Salin V., and Dallakyan A. (2018) The Use of Time Series Analysis in Examining Food Safety Issues.Journal of Food Distribution Research.2(49) 57-80

Bakhtavoryan R., Dallakyan A., and M. Galstyan.(2016)Analysis of Factors Impacting Rural Women’s Labor Force Participation in Armenia.Collected Articles on the Problems of Sustained Social-Economic Development of Republic of Armenia.1 (23)309-322