5 citations · 9 across the 6 of their papers we have counts for
6 papers
Scalable and non-iterative graphical model estimation
Kshitij Khare, Syed Rahman, Bala Rajaratnam +1
Graphical models have found widespread applications in many areas of modern statistics and machine learning. Iterative Proportional Fitting (IPF) and its variants have become the d…
Estimation of Gaussian directed acyclic graphs using partial ordering information with an application to dairy cattle data
Syed Rahman, Kshitij Khare, George Michailidis +2
Estimating a directed acyclic graph (DAG) from observational data represents a canonical learning problem and has generated a lot of interest in recent years. Research has focused…
Inferring the Partial Correlation Structure of Allelic Effects and Incorporating it in Genome-wide Prediction
Carlos A. Martínez, Kshitij Khare, Syed Rahman +1
In this study, we addressed the problem of genome-wide prediction accounting for partial correlation of marker effects when the partial correlation structure, or equivalently, the…
Introducing Gaussian covariance graph models in genome-wide prediction
Carlos Alberto Martínez, Kshitij Khare, Syed Rahman +1
Several statistical models used in genome-wide prediction assume independence of marker allele substitution effects, but it is known that these effects might be correlated. In stat…
Modelling correlated marker effects in genome-wide prediction via Gaussian concentration graph models
Carlos Alberto Martínez, Kshitij Khare, Syed Rahman +1
In genome-wide prediction, independence of marker allele substitution effects is typically assumed; however, since early stages of this technology it has been known that nature poi…
A convex framework for high-dimensional sparse Cholesky based covariance estimation
Kshitij Khare, Sang Oh, Syed Rahman +1
Covariance estimation for high-dimensional datasets is a fundamental problem in modern day statistics with numerous applications. In these high dimensional datasets, the number of…