EFFICIENT ESTIMATION OF GENERALIZED ADDITIVE NONPARAMETRIC REGRESSION MODELS

By Oliver B. Linton. Econometric Theory, 2000

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Local Likelihood Estimation in Generalized Additive Models (2003, methodology): “Using this estimator as a starting point, Linton (2000) proposed a local likelihood procedure to find a fully efficient estimator for individual additive component functions when the remaining component functions are suitably undersmoothed (see also Fan et al., 1998b).”
A flexible semiparametric forecasting model for time series (2015, background): “…by w0 +∑d j=1wjE(Y |Xj) is a linear subspace of the space of additive functions and so we cannot do as well as unrestricted additive fitting (see, Nielsen and Linton 1998, Mammen et al 1999, Linton 2000, and Nielsen and Sperlich 2005 for more discussion on estimation of the additive models).”
Endogenous selection or treatment model estimation (2007, methodology): “Based on this work, and using results in Masry (1996a), (1996b), and Gozalo and Linton (2000), Lewbel, Linton, and McFadden (2001) provide the limiting normal distribution for δ1δ0 m(v, x)dv − δ1 δ0 m(v, x)dv, both under high level assumptions regarding the nonparametric regression estimator m(v,…”
Optimal estimation in additive regression models (2006, background): “Linton (2000) and Horowitz and Mammen (2004) present other two-step procedures that have the same asymptotic bias and variance as an oracle estimator.”