On a threshold heteroscedastic model

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Forecasting: theory and practice (2020, background): “agged value of the dependent variable. In the latter case, we speak of self-exciting threshold autoregressive (SETAR) models. Other choices of q t include linear (see, for example, Chen et al., 2003; Chen and So, 2006; Gerlach et al., 2006) or non-linear (see, for example, Chen, 1995; Wu and Chen, 2”
Bayesian causality test for integer‐valued time series models with applications to climate and crime data (2017, methodology): “We base the procedures of random-walk MH and independent kernel MH methods for this work on those of Chen and So (2006).”
Bayesian Time-Varying Quantile Forecasting for Value-at-Risk in Financial Markets (2011, methodology): “See Chen and So (2006) for more details of the Gaussian proposal case, but the Student-t proposals employed here will further assist in achieving coverage and mixing, over the Gaussian, for both the burn-in and sampling periods, by lowering the probability of getting stuck in local modes for long pe”
Conditional (2019, unknown): “To achieve a high degree of differentiation from the original identity while preserving the realistic essence of face appearance, we employ a Latent Diffusion-based ID Sampler, which is able to sample anonymized identities from the distribution of real human identities.”