3 citations · 3 across the 5 of their papers we have counts for
7 papers
Is the regression -test doubly robust?
Lucy Xia, Oliver Y. Feng, Yang Feng +2
We study the robustness of the -test in random design linear models, and reach a somewhat nuanced conclusion. On the positive side, one of our main results is that the size of t…
Learning the score under shape constraints
Rebecca M. Lewis, Oliver Y. Feng, Henry W. J. Reeve +2
Score estimation has recently emerged as a key modern statistical challenge, due to its pivotal role in generative modelling via diffusion models. Moreover, it is an essential ingr…
Upgrading survival models with CARE
William G. Underwood, Henry W. J. Reeve, Oliver Y. Feng +3
Clinical risk prediction models are regularly updated as new data, often with additional covariates, become available. We propose CARE (Convex Aggregation of relative Risk Estimato…
Optimal convex -estimation via score matching
Oliver Y. Feng, Yu-Chun Kao, Min Xu +1
In the context of linear regression, we construct a data-driven convex loss function with respect to which empirical risk minimisation yields optimal asymptotic variance in the dow…
Nonparametric, tuning-free estimation of S-shaped functions
Oliver Y. Feng, Yining Chen, Qiyang Han +2
We consider the nonparametric estimation of an S-shaped regression function. The least squares estimator provides a very natural, tuning-free approach, but results in a non-convex…
A unifying tutorial on Approximate Message Passing
Oliver Y. Feng, Ramji Venkataramanan, Cynthia Rush +1
Over the last decade or so, Approximate Message Passing (AMP) algorithms have become extremely popular in various structured high-dimensional statistical problems. The fact that th…
Adaptation in multivariate log-concave density estimation
Oliver Y. Feng, Adityanand Guntuboyina, Arlene K. H. Kim +1
We study the adaptation properties of the multivariate log-concave maximum likelihood estimator over three subclasses of log-concave densities. The first consists of densities with…