Sujit Ghosh

North Carolina State University, Statistics, Faculty Member

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Professor Sujit Kumar Ghosh, a tenured Professor in the Department of Statistics at North Carolina State University (NCSU) in Raleigh, NC, USA, has over 30 years of experience in conducting, applying, evaluating and documenting statistical analysis of biomedical and environmental data. Prof. Ghosh is actively involved in teaching, supervising and mentoring graduate students at the doctoral and master levels. He has supervised over 45 doctoral graduate students and 5 post-doctoral fellows and he has served as a member on numerous other doctoral and master level committees. Prof. Ghosh has also served as a statistical investigator and consultant for over 50 different research projects funded by various leading federal agencies and private industries. Prof. Ghosh has published over 140 refereed journal articles in the various areas of statistics with applications in biomedical and environmental sciences, econometrics and engineering. He has given over 200 invited lectures, seminars and short courses at national and international meetings. He has also served as the short term visiting professor at leading institutions in various countries (Greece, India, Italy, Singapore, Thailand, Turkey etc.). In recent years, Prof. Ghosh has contributed significantly in developing statistical models and associated methodologies for various inferential problems that are subject to shape constraint. Prof. Ghosh received the International Indian Statistical Association (IISA) Young Investigator Award in 2008.
Supervisors: Alan E. Gelfand, Duke University
Phone: 9195152570
Address: 2311 K. Stinson Drive (5116 SAS Hall)
Department of Statistics,
NC State University
Raleigh, NC 27695-8203, USA

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Scalable Resampling in Massive Generalized Linear Models via Subsampled Residual Bootstrap

arXiv (Cornell University), Jul 13, 2023

Residual bootstrap is a classical method for statistical inference in regression settings. With m... more Residual bootstrap is a classical method for statistical inference in regression settings. With massive data sets becoming increasingly common, there is a demand for computationally efficient alternatives to residual bootstrap. We propose a simple and versatile scalable algorithm called subsampled residual bootstrap (SRB) for generalized linear models (GLMs), a large class of regression models that includes the classical linear regression model as well as other widely used models such as logistic, Poisson and probit regression. We prove consistency and distributional results that establish that the SRB has the same theoretical guarantees under the GLM framework as the classical residual bootstrap, while being computationally much faster. We demonstrate the empirical performance of SRB via simulation studies and a real data analysis of the Forest Covertype data from the UCI Machine Learning Repository.

Sujit Ghosh

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