activity
20182026
collaborators

7 papers

stat.ME2026

Improving the Power of Bonferroni Adjustments under Joint Normality and Exchangeability

Caleb Hiltunen, Yeonwoo Rho

Bonferroni's correction is a popular tool to address multiplicity but is notorious for its low power when tests are dependent. This paper proposes a practical modification of Bonfe…

stat.ME2026

Change point analysis of high-dimensional data using random projections

Yi Xu, Yeonwoo Rho

This paper develops a novel change point identification method for high-dimensional data using random projections. By projecting high-dimensional time series into a one-dimensional…

math.ST2024

Heavy-tailed -value combinations from the perspective of extreme value theory

Yeonwoo Rho

Handling multiplicity without losing much power has been a persistent challenge in various fields that often face the necessity of managing numerous statistical tests simultaneousl…

stat.ME2021

Stable combination tests

Xing Ling, Yeonwoo Rho

This paper proposes a stable combination test, which is a natural extension of Cauchy combination tests by Liu and Xie (2020). Similarly to the Cauchy combination test, our stable…

econ.EM2020

Revealing Cluster Structures Based on Mixed Sampling Frequencies

Yeonwoo Rho, Yun Liu, Hie Joo Ahn

This paper proposes a new linearized mixed data sampling (MIDAS) model and develops a framework to infer clusters in a panel regression with mixed frequency data. The linearized MI…

econ.EM2018

On the Choice of Instruments in Mixed Frequency Specification Tests

Yun Liu, Yeonwoo Rho

Time averaging has been the traditional approach to handle mixed sampling frequencies. However, it ignores information possibly embedded in high frequency. Mixed data sampling (MID…

econ.EM2018

Bootstrap-Assisted Unit Root Testing With Piecewise Locally Stationary Errors

Yeonwoo Rho, Xiaofeng Shao

In unit root testing, a piecewise locally stationary process is adopted to accommodate nonstationary errors that can have both smooth and abrupt changes in second- or higher-order…