Pankaj Bhagwat

Research

Research Areas

Trustworthy Machine Learning, Non-Euclidean data analysis, Statistical decision theory, Bayesian Statistics

STAT 537 - Statistical Methods for Applied Research II

Review of basic statistical concepts of inference and probability theory. Includes applied methods of Linear and non-linear regression and analysis of variance for designed experiments, multiple comparisons, correlations, modeling and variable selection, multicollinearity, predictions, confounding and Simpson's paradox. Includes case studies and real data applications. Each researcher works on a project to present, highlighting the methods used in the project. Prerequisite: STAT 437 equivalent or consent of the instructor.

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STAT 566 - Methods of Statistical Inference

An introduction to the theory of statistical inference. Topics to include exponential families and general linear models, likelihood, sufficiency, ancillarity, interval and point estimation, asymptotic approximations. Optional topics as time allows, may include Bayesian methods, Robustness, resampling techniques. This course is intended primarily for MSc students. Prerequisite: STAT 471 or consent of Department.

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Browse more courses taught by Pankaj Bhagwat

Enze Shi, Pankaj Bhagwat, Zhixian Yang, Linglong Kong, Bei Jiang

The Thirty-ninth Annual Conference on Neural Information Processing Systems. 2026 January;

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Bhagwat, P., Kong, L., Jiang, B.

13th International Conference on Learning Representations Iclr 2025. 2025 January;

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Bhagwat, Pankaj, Marchand, Éric

Electronic Journal of Statistics. 2023 January; 10.1214/23-EJS2142

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Bhagwat, Pankaj, Marchand, Éric

Journal of Multivariate Analysis. 2023 January; 10.1016/j.jmva.2023.105190