Sayantan Banerjee

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We consider the problem of estimating a sparse precision matrix of a multivariate Gaussian distribution, including the case where the dimension p exceeds the sample size n. Gaussian graphical models provide an important tool in describing conditional independence through presence or absence of the edges in the underlying graph. A popular non-Bayesian method(More)
Variable selection techniques for the classical linear regression model have been widely investigated. Variable selection in fully nonparametric and additive regression models has been studied more recently. A Bayesian approach for nonparametric additive regression models is considered, where the functions in the additivemodel are expanded in a B-spline(More)
We consider Bayesian estimation of a p×p precision matrix, where p can be much larger than the available sample size n. It is well known that consistent estimation in such an ultra-high dimensional situation requires regularization such as banding, tapering or thresholding. We consider a banding structure in the model and induce a prior distribution on a(More)
Tumor heterogeneity is a crucial area of cancer research wherein inter- and intra-tumor differences are investigated to assess and monitor disease development and progression, especially in cancer. The proliferation of imaging and linked genomic data has enabled us to evaluate tumor heterogeneity on multiple levels. In this work, we examine magnetic(More)
Variable selection in regression models have been well studied in the literature, with a number of non-Bayesian and Bayesian methods available in this regard. An important class of regression models is generalized linear models, which involve situations where the response variable is discrete. To add more flexibility, generalized additive partial linear(More)
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