Doubly penalized likelihood estimator in heteroscedastic regression

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Recent Advances in Bayesian Optimization (2022, unknown): “Similar studies include [148, 204, 276].”
Heteroscedastic Gaussian process regression (2005, methodology): “In the following we compare our approach, as described in Section 2.3 to the heteroscedastic estimators of ( Yuan & Wahba, 2004; Cawley et al., 2003).”
Most likely heteroscedastic Gaussian process regression (2007, methodology): “Yuan and Wahba (2004) also jointly estimate the mean and noise variances but do not deal with the problem of selecting the kernel function.”
Learning Non-stationary System Dynamics Online Using Gaussian Processes (2010, background): “Table 3 shows typical results for two synthetic data sets A and B; and a data set of a simulated motor-cycle crash C, which are introduced in detail in [2], [14], and [15], respectively.”
Smoothing Splines: Methods and Applications (2011, background): “24) jointly as the minimizers of the following doubly penalized likelihood (DPL) (Yuan and Wahba 2004):”
Gaussian Process Training with Input Noise (2011, methodology): “’s Monte Carlo training method with a “most likely” training scheme and demonstrated its effectiveness; related work includes Yuan and Wahba [4], and Le at al.”