2 citations · 7 across the 12 of their papers we have counts for
5 papers · 1 filter
Graphical Log-linear Models: Fundamental Concepts and Applications
Niharika Gauraha
We present a comprehensive study of graphical log-linear models for contingency tables. High dimensional contingency tables arise in many areas such as computational biology, colle…
Mutual Conditional Independence and its Applications to Inference in Markov Networks
Niharika Gauraha
The fundamental concepts underlying in Markov networks are the conditional independence and the set of rules called Markov properties that translates conditional independence const…
Efficient Clustering of Correlated Variables and Variable Selection in High-Dimensional Linear Models
Niharika Gauraha, Swapan K. Parui
In this paper, we introduce Adaptive Cluster Lasso(ACL) method for variable selection in high dimensional sparse regression models with strongly correlated variables. To handle cor…
Model Selection for Graphical Log-linear Models: A Forward Model Selection Algorithm based on Mutual Conditional Independence
Niharika Gauraha
Model selection and learning the structure of graphical models from the data sample constitutes an important field of probabilistic graphical model research, as in most of the situ…
Constraints and Conditions: the Lasso Oracle-inequalities
Niharika Gauraha
We study various constraints and conditions on the true coefficient vector and on the design matrix to establish non-asymptotic oracle inequalities for the prediction error, estima…