research

1. Sequential Treatment Allocation with Interference

We study the sequential treatment allocation problem (a.k.a. multi armed bandit problem) with interference. The work is motivated from the recent work of Jia et al. (2024). We propose some new algorithms for this problem and investigate their performance in the bandit setting.

2. Bias correction, Bootstrap and Kernel Estimators

We propose a weighted bootstrap based confidence interval for the bias corrected local linear estimator. The idea of bias correction for the kernel estimators is motivated from the work of Calonico, Cattaneo, & Farrell (2018) and Cheng & Chen (2019). Here Cheng & Chen (2019) already proposed bias corrected paired bootstrap and proved theoretical results for uniform confidence intervals. In our work we propose a weighted bootstrap, and investigate the point-wise performance.

3. Autoregressive Dynamic Network Modeling with Serial and Cross Sectional Dependence

This is a joint work with my supervisor Professor Carsten Jentsch and an extension of my master thesis.

I will provide the draft version of the papers soon…