181 citations · 189 across the 11 of their papers we have counts for
11 papers
Adaptive Bayesian Variable Clustering via Structural Learning of Breast Cancer Data
Riddhi Pratim Ghosh, Arnab Kumar Maity, Mohsen Pourahmadi +1
Clustering of proteins is of interest in cancer cell biology. This article proposes a hierarchical Bayesian model for protein (variable) clustering hinging on correlation structure…
Estimation of Constrained Mean-Covariance of Normal Distributions
Anupam Kundu, Mohsen Pourahmadi
Estimation of the mean vector and covariance matrix is of central importance in the analysis of multivariate data. In the framework of generalized linear models, usually the varian…
Time Series Graphical Lasso and Sparse VAR Estimation
Aramayis Dallakyan, Rakheon Kim, Mohsen Pourahmadi
We improve upon the two-stage sparse vector autoregression (sVAR) method in Davis et al. (2016) by proposing an alternative two-stage modified sVAR method which relies on time seri…
Learning Bayesian Networks through Birkhoff Polytope: A Relaxation Method
Aramayis Dallakyan, Mohsen Pourahmadi
We establish a novel framework for learning a directed acyclic graph (DAG) when data are generated from a Gaussian, linear structural equation model. It consists of two parts: (1)…
MLE of Jointly Constrained Mean-Covariance of Multivariate Normal Distributions
Anupam Kundu, Mohsen Pourahmadi
Estimating the unconstrained mean and covariance matrix is a popular topic in statistics. However, estimation of the parameters of under joint constraints such as $Σμ= μ…
Fused-Lasso Regularized Cholesky Factors of Large Nonstationary Covariance Matrices of Longitudinal Data
Aramayis Dallakyan, Mohsen Pourahmadi
Smoothness of the subdiagonals of the Cholesky factor of large covariance matrices is closely related to the degrees of nonstationarity of autoregressive models for time series and…
Stationary subspace analysis of nonstationary covariance processes: eigenstructure description and testing
Raanju Ragavendar Sundararajan, Vladas Pipiras, Mohsen Pourahmadi
Stationary subspace analysis (SSA) searches for linear combinations of the components of nonstationary vector time series that are stationary. These linear combinations and their n…
Baxter's inequality for finite predictor coefficients of multivariate long-memory stationary processes
Akihiko Inoue, Yukio Kasahara, Mohsen Pourahmadi
For a multivariate stationary process, we develop explicit representations for the finite predictor coefficient matrices, the finite prediction error covariance matrices and the pa…
The intersection of past and future for multivariate stationary processes
Akihiko Inoue, Yukio Kasahara, Mohsen Pourahmadi
We consider an intersection of past and future property of multivariate stationary processes which is the key to deriving various representation theorems for their linear predictor…
Covariance Estimation: The GLM and Regularization Perspectives
Mohsen Pourahmadi
Finding an unconstrained and statistically interpretable reparameterization of a covariance matrix is still an open problem in statistics. Its solution is of central importance in…
Applications of a finite-dimensional duality principle to some prediction problems
Yukio Kasahara, Mohsen Pourahmadi, Akihiko Inoue
Some of the most important results in prediction theory and time series analysis when finitely many values are removed from or added to its infinite past have been obtained using d…