Weighted Nadaraya–Watson regression estimation

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Learning to (Learn at Test Time): RNNs with Expressive Hidden States (2024, unknown): “Consider the TTT layer with the Nadaraya-Watson estimator [6, 11], defined as: where y s = θ V x s is the label view discussed in Subsection 2.3, and is a kernel with bandwidth hyper-parameters θ K and θ Q .”
Tilted Nonparametric Estimation of Volatility Functions With Empirical Applications (2010, unknown): “Because an absolutely regular time series is α-mixing, lemma A2 of Cai (2001) holds under our assumptions, that is, λ = − hK1f ′(x) υ2f (x) + Oa.”
On Conditional Density Estimation (2003, background): “CAI (2001, 2002) also extended the smoother of Hall et al. (1999) to contexts other than the distribution function mainly to conditional quantiles.”
Nonparametric estimation of conditional VaR and expected shortfall (2008, methodology): “Finally, Assumptions B1–B5 are also imposed in Cai (2001).”
The effects of a “black swan” event (COVID-19) on herding behavior in cryptocurrency markets (2021, unknown): “This preserves the bias, variance, and automatic good boundary behavior properties of the local linear estimator, ensuring flexibility and robustness (Cai, 2001).”