Copula-Based Nonlinear Quantile Autoregression

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Agricultural Drought Prediction Based on Conditional Distributions of Vine Copulas (2021, background): “where τ is known as the copula–quantile curve (τth curve) (Chen et al., 2009), and its interval range is [0, 1], which can be considered a random probability level of the conditional cumulative distribution function; h is the inverse form of the h-function; and N denotes the inverse Gaussian distrib”
A review of copula models for economic time series (2012, unknown): “[28], Chen et al.”
A multivariate conditional model for streamflow prediction and spatial precipitation refinement (2015, methodology): “95), we may derive u2 from Cu2 u1 j using an explicit function u2 1⁄4 u2 1⁄4 C 1 u2ju1 τ; u1 ð Þ 1⁄4 h 1 τ u1Þ j ð , where C 1 u2 ju1 is the inverse of the copula function known as the τ quantile curve of the copula [Chen et al., 2009; Xu and Childs, 2013].”