Sangyeol Lee

Professor

Seoul National University

South Korea

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About

Professor Sangyeol Lee is a faculty member at Seoul National University in South Korea. His research focuses on time series analysis, particularly in the areas of bivariate signed integer-valued autoregressive models and conditionally heteroscedastic time series. Professor Lee has authored several articles that explore advanced monitoring processes and control chart methodologies for multivariate count data.

Articles (18)

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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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2023

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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

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2022

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2022

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Sangyeol Lee

Research Interests

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About

Articles (18)

Sequential monitoring process for bivariate signed integer-valued autoregressive models

Kernel-based composite control chart for nonlinear conditionally heteroscedastic time series

Modeling and inferences for bivariate signed integer-valued autoregressive models

Multiple values-inflated bivariate INAR time series of counts: featuring zero–one inflated Poisson-Lindly case

Sequential monitoring for conditional quantiles of general conditional heteroscedastic time series models

Modeling and inferences for bounded multivariate time series of counts

Robust control chart for nonlinear conditionally heteroscedastic time series based on Huber support vector regression

Bivariate random coefficient integer‐valued autoregressive models: Parameter estimation and change point test

Monitoring parameter change for bivariate time series models of counts

Statistical process monitoring for vector autoregressive time series based on location-scale CUSUM method

Multiple values-inflated time series of counts: modeling and inference based on INGARCH scheme

Exponential family QMLE-based CUSUM test for integer-valued time series

Change point test for structural vector autoregressive model via independent component analysis

Comprehensive interval‐valued time series model with application to the S&P 500 index and PM2.5 level data analysis

Conditional quantile change test for time series based on support vector regression

Monitoring parameter change for time series models with application to location-Scale heteroscedastic models

Maximum composite likelihood estimation for spatial extremes models of Brown–Resnick type with application to precipitation data

Mean targeting estimation for integer-valued time series with application to change point test

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