Under review

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

Dallakyan A. and Yang Ni (2024+). Generalized Criterion for Identifiability of Additive Noise Models Using Majorization. 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. (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