A comparison study of penalized likelihood via regularization and support vector-based control charts

Maboudou-Tchao, Edgard M.; Harrison, Charles W.; Sen, Sumen

doi:10.1080/16843703.2022.2096198

Cited by 4 publications

(1 citation statement)

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“…Among those, we pay special attention to one-class classification (OCC)-based control charts, which are designed to identify the boundary of a specific class among all data points via learning from a training dataset. For relevant works, we refer to [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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

Kim,

Yoon,

Lee

2024

PLoS ONE

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This study proposes a control chart that monitors conditionally heteroscedastic time series by integrating the Huber support vector regression (HSVR) and the one-class classification (OCC) method. For this task, we consider the model that incorporates nonlinearity to the generalized autoregressive conditionally heteroscedastic (GARCH) time series, named HSVR-GARCH, to robustly estimate the conditional volatility when the structure of time series is not specified with parameters. Using the squared residuals, we construct the OCC-based control chart that does not require any posterior modifications of residuals unlike previous studies. Monte Carlo simulations reveal that deploying squared residuals from the HSVR-GARCH model to control charts can be immensely beneficial when the underlying model becomes more complicated and contaminated with noises. Moreover, a real data analysis with the Nasdaq composite index and Korea Composite Stock Price Index (KOSPI) datasets further disclose the validity of using the bootstrap method in constructing control charts.

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Section: Introductionmentioning

confidence: 99%