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Gene hunting with knockoffs for hidden Markov models

Published in Biometrika, 2019

This paper develop an exact construction of knockoffs for variables distributed as hidden Markov models, and builds upon this a principled and versatile method for controlling the false discovery rate in genome-wide association studies.

Recommended citation: Sesia, Sabatti, and Candès. (2019). "Gene hunting with knockoffs for hidden Markov models." Biometrika. 106(1), pages 1-18. https://doi.org/10.1093/biomet/asy033

Deep Knockoffs

Published in Journal of the American Statistical Association, 2019

This paper introduces a machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models. By building upon the existing model-X framework, we thus obtain a flexible and model-free statistical tool to perform controlled variable selection.

Recommended citation: Romano, Sesia, and Candès. (2019). "Deep Knockoffs." J. Am. Stat. Assoc.. 0(0), pages 1-12. https://doi.org/10.1080/01621459.2019.1660174

A comparison of some conformal quantile regression methods

Published in Stat, 2020

This paper compares two recent methods that combine conformal inference with quantile regression to produce locally adaptive and marginally valid prediction intervals under sample exchangeability, both theoretically and empirically.

Recommended citation: Sesia and Candès. (2020). "A comparison of some conformal quantile regression methods." Stat. 9:e261. http://dx.doi.org/10.1002/sta4.261

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