Mathematical and Physical Sciences

Professor Chetkar Jha has worked as a postdoctoral research associate at Washington University in St Louis (WashU), where he taught multiple courses, including statistical computing, probability, linear models, and Bayesian analysis. At WashU, he also worked with Dr Debashish Mondal on network problems and Bayesian analysis. Before joining WashU, he was a postdoctoral researcher at the University of Pennsylvania (UPenn) in the Department of Biostatistics, Epidemiology, and Informatics, where he worked with Dr Ian Barnett on network analysis and high-dimensional problems. Their work on network analysis won the best poster prize in the Biomedical Postdoc Symposium at UPenn and was invited to the centenary celebration of Dr Renyi at the Alfred Renyi Institute of Mathematics. He earned his PhD in Statistics from the Department of Statistics at the University of Missouri-Columbia (Mizzou), under the supervision of Dr Dongchu Sun, where he worked on deriving objective priors and nonparametric Bayesian methods. He has completed his undergraduate and graduate studies in Statistics from Indian Statistical Institute, Kolkata.

• Jha, C., Li, Y., and Guha, S. (2017). Semiparametric Bayesian analysis of high-dimensional censored outcome data. Statistical Theory and Related Fields 1(2), 194-204.
• Jha, C., and Sun, D. (2024) “A general scheme for deriving conditional reference priors”. Bayesian Analysis, 19(1) 153-179.
• Jha, C., Li, M. and Barnett, I. (2022). Multiple testing to estimate the number of communities in sparse stochastic block models with application to transcriptomics. (Invited Revision at Annals of Applied Statistics)

Publications (In preparation)

• Jha, C., and Barnett, I. Confidence intervals for the number of spiked components in factor analysis and principal component analysis via subsampling. (In preparation)
• Jha, C. A dynamic Bayesian nonparametric model for modeling temporal interactions in Panel data. (In preparation)
• Jha, C., and Barnett, I. A nonparametric method for estimating the number of components in factor analysis and principal component analysis. (In preparation)
• Jha, C., Jana, I., and Sengupta, S. A nonparametric test of co-spectrality of networks. (In preparation)
• Cao, A., and Jha, C. Estimating number of factors in factor analysis for datasets with missing entries at random. (In preparation)

Publications (Work in Progress)

• Jha, C., and Mondal, D. An extension of sequential multiple tests to degree corrected models. (Work in Progress)
• Jha, C., Barron, A., and Lahiri, A., et al. A Bayesian nonparametric model for matching datasets. (Work in Progress)