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Haeran Cho

Academic , Professor of Statistical Science

University of Bristol

United Kingdom

Research Interests

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About

Haeran Cho is an Associate Professor in Statistics at The University of Bristol, UK. With a research focus on high-dimensional data analysis, nonparametric data segmentation, and change point detection, Professor Cho has published numerous articles addressing complex statistical methodologies. His recent work includes contributions to the detection of changes in regression models and advancements in forecasting for high-dimensional time series.

Articles (18)

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2026

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2025

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2025

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2025

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2025

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2025

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2025

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2024

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2024

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2024

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2024

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2023

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2023

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2023

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2023

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2022

Collaborators (7)

Euan McGonigle

Lecturer in Statistics

University of Southampton

UNITED KINGDOM

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Haotian Xu

Assistant Professor

Auburn University

UNITED STATES

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Idris Arthur Eckley

University of Lancaster

UNITED KINGDOM

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Joonpyo Kim

Assistant Professor

Sejong University

SOUTH KOREA

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Paul Fearnhead

University of Lancaster

UNITED KINGDOM

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Lorenzo Trapani

Professor of Econometrics

University of Pavia

ITALY

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Hyeyoung Maeng

Assistant Professor

University of Durham

UNITED KINGDOM

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Haeran Cho

Research Interests

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About

Articles (18)

Moving Sum Procedure for Multiple Change Point Detection in Large Factor Models

Capturing usage patterns in bike sharing system via multilayer network fused Lasso

Detection and inference of changes in high-dimensional linear regression with nonsparse structures

Detection and inference of changes in high-dimensional linear regression with nonsparse structures

Nonparametric data segmentation in multivariate time series via joint characteristic functions

Nonparametric data segmentation in multivariate time series via joint characteristic functions

Tail-robust factor modelling of vector and tensor time series in high dimensions

Estimation and Inference for Change Points in Functional Regression Time Series

FNETS: Factor-Adjusted Network Estimation and Forecasting for High-Dimensional Time Series

Moving Sum Procedure for Change Point Detection under Piecewise Linearity

High-dimensional data segmentation in regression settings permitting temporal dependence and non-Gaussianity

Multiple change point detection under serial dependence: Wild contrast maximisation and gappy Schwarz algorithm

Data segmentation algorithms: Univariate mean change and beyond

High-dimensional time series segmentation via factor-adjusted vector autoregressive modelling

FNETS: Factor-adjusted network estimation and forecasting for high-dimensional time series

Robust multiscale estimation of time-average variance for time series segmentation

fnets: An R Package for Network Estimation and Forecasting via Factor-Adjusted VAR Modelling

Bootstrap confidence intervals for multiple change points based on moving sum procedures

Collaborators (7)

Euan McGonigle

Haotian Xu

Idris Arthur Eckley

Joonpyo Kim

Paul Fearnhead

Lorenzo Trapani

Hyeyoung Maeng

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