Testing additivity in generalized nonparametric regression models with estimated parameters

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A Test for model specification of diffusion processes (2007, methodology): “Kernel-based tests have been shown to be effective in testing discrete time series models as demonstrated in Robinson [44], Fan and Li [24], Hjellvik, Yao and Tjøstheim [30], Li [38], Aït-Sahalia, Bickel and Stoker [4], Gozalo and Linton [27] and Chen, Härdle and Li [12]; see Hart [29] and Fan and Y”
Nonparametric Inferences for Additive Models (2005, methodology): “The basic idea is inspired by the prewhitening technique of Press and Tukey (1956) in spectral density estimation and the technique used by Härdle and Mammen (1993) for univariate nonparametric testing.”
An updated review of Goodness-of-Fit tests for regression models (2013, background): “Motivated by the incorrelation between ε0 and (m(X) − m0(X)) (see Gozalo and Linton 2001), the following test statistic: T AM∗1n = 1 n n ∑ i=1 ( m̂(Xi) − m̂0(Xi) ) ε̂i0 (27) has asymptotic bias K(0) ∫ σ 2(x) dx and asymptotic variance 2 ∫ K2(x) dx ∫ σ 4(x) dx.”
Checking the adequacy of a partial linear model (2003, background): “Gozalo and Linton (2001), in a related work, employed generalized cross-validation (GCV) to select the bandwidth without arguing its use.”
Nonparametric Estimation of an Additive Quantile Regression Model (2004, methodology): “mean regression model are available (see, for example, Gozalo and Linton (2001) ), but these”