activity
20152017
most citedWhy Adaptively Collected Data Have Negative Bias and How to Correct for It

23 citations · 81 across the 5 of their papers we have counts for

collaborators

5 papers

stat.ML2017★ 23 cited

Why Adaptively Collected Data Have Negative Bias and How to Correct for It

Xinkun Nie, Xiaoying Tian, Jonathan Taylor +1

From scientific experiments to online A/B testing, the previously observed data often affects how future experiments are performed, which in turn affects which data will be collect…

math.ST2016★ 13 cited

MAGIC: a general, powerful and tractable method for selective inference

Xiaoying Tian, Nan Bi, Jonathan Taylor

Selective inference is a recent research topic that tries to perform valid inference after using the data to select a reasonable statistical model. We propose MAGIC, a new method f…

math.ST2015★ 23 cited

Selective inference with a randomized response

Xiaoying Tian, Jonathan E. Taylor

Inspired by sample splitting and the reusable holdout introduced in the field of differential privacy, we consider selective inference with a randomized response. We discuss two ma…

math.ST2015★ 17 cited

Selective inference with unknown variance via the square-root LASSO

Xiaoying Tian, Joshua R. Loftus, Jonathan E. Taylor

There has been much recent work on inference after model selection when the noise level is known, however, is rarely known in practice and its estimation is difficult in high-d…

math.ST2015★ 5 cited

Asymptotics of selective inference

Xiaoying Tian, Jonathan Taylor

In this paper, we seek to establish asymptotic results for selective inference procedures removing the assumption of Gaussianity. The class of selection procedures we consider are…