I am interested in developing robust applicable statistical procedures for high-dimensional data, with rigorous theoretical guarantees, under minimal assumptions. My graduate research focuses on the study of likelihood based inference in high-dimensional generalized linear models. In particular, it uncovers that for a class of generalized linear models, classical maximum likelihood theory fails to hold in a high-dimensional setup. Consequently, p-values/confidence intervals obtained from standard statistical software packages are often unreliable. Some illustrations to this end can be found here.

In a series of papers, I have studied several aspects of this phenomenon and have developed a modern maximum-likelihood theory suitable for high-dimensional data that accurately characterizes properties of likelihood based approaches and that can be used by practitioners to obtain valid inference for such non-linear models.

I also have a standing interest in controlled variable selection and possible connections to causal inference. I am simultaneously involved in research on the different definitional aspects of algorithmic fairness, their connections, limitations and implementations, and robust metric learning for fairness.

P. Sur and E. J. Candès. ** A modern maximum-likelihood theory for high-dimensional logistic regression**. Under revision, *Proceedings of the National Academy of Sciences,* 2018+.
[pdf]
[supp]
[arXiv]

E. J. Candès and P. Sur. ** The phase transition for the existence of the maximum likelihood estimate in high-dimensional logistic regression**. * The Annals of Statistics*, to appear, 2018+.
[pdf]
[arXiv]

P. Sur, Y. Chen, and E. J. Candès. ** The likelihood ratio test in high-dimensional logistic regression is asymptotically a rescaled chi-square**.
* Probability Theory and Related Fields*, to appear, 2018+.
[pdf]
[supp]
[talk]
[arXiv]

P. Sur, G. Shmueli, S. Bose, and P. Dubey. ** Modeling bimodal discrete data using Conway-
Maxwell-Poisson mixture models**.
* Journal of Business and Economic Statistics*, Volume 33, 2015 - Issue 3.
[pdf]
[journal]

S. Bose, G. Shmueli, P. Sur, and P. Dubey. ** Fitting COM-Poisson mixtures to bimodal count data**.
* Proceedings of the 2013 International Conference on Information, Operations Management and Statistics (ICIOMS 2013),* winner of Best Paper Award.
[conference]