22 citations · 26 across the 5 of their papers we have counts for
5 papers
Data fusion using weakly aligned sources
Sijia Li, Peter B. Gilbert, Rui Duan +1
We introduce a new data fusion method that utilizes multiple data sources to estimate a smooth, finite-dimensional parameter. Most existing methods only make use of fully aligned d…
Scalable Bigraphical Lasso: Two-way Sparse Network Inference for Count Data
Sijia Li, Martín López-García, Neil D. Lawrence +1
Classically, statistical datasets have a larger number of data points than features (). The standard model of classical statistics caters for the case where data points are…
Efficient Estimation Under Data Fusion
Sijia Li, Alex Luedtke
We aim to make inferences about a smooth, finite-dimensional parameter by fusing data from multiple sources together. Previous works have studied the estimation of a variety of par…
Estimating the Efficiency Gain of Covariate-Adjusted Analyses in Future Clinical Trials Using External Data
Xiudi Li, Sijia Li, Alex Luedtke
We present a general framework for using existing data to estimate the efficiency gain from using a covariate-adjusted estimator of a marginal treatment effect in a future randomiz…
Discussion of Kallus (2020) and Mo, Qi, and Liu (2020): New Objectives for Policy Learning
Sijia Li, Xiudi Li, Alex Luedtke
We discuss the thought-provoking new objective functions for policy learning that were proposed in "More efficient policy learning via optimal retargeting" by Nathan Kallus and "Le…