NONPARAMETRIC ESTIMATION AND TESTING OF INTERACTION IN ADDITIVE MODELS

By Stefan Sperlich, Dag Tjøstheim, Lijian Yang. Econometric Theory, 2002

159 citations recorded by the source corpus.

Subfields: Economics and Econometrics, General Agricultural and Biological Sciences, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6d66a4cca6bfe538226cc2c46c06a1fc68ef6f10

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Citations in the corpus

Rate optimal estimation with the integration method in the presence of many covariates (2005, background): “While this estimator seems to work well in practice when the true function is additive (see for example [22] or [25]), the theory we presented does not quite apply because we require Q to be a product measure.”
Estimation of a semiparametric transformation model (2008, background): “and Auestad (1994), Linton and Nielsen (1995), and Sperlich, Tjostheim and Yang (2002) for”
Determination of linear components in additive models (2011, unknown): “Barry (1993), Chen, Liu, and Tsay (1995), Härdle and Tsybakov (1995), Sperlich, Tjøstheim, and Yang (2002), and Härdle, Huet, Mammen, and Sperlich (2004) have proposed methods to test for additivity, and thereby obtain diagnostic fools for using additive models in practice.”
NONPARAMETRIC ADDITIVE MODELS FOR PANELS OF TIME SERIES (2009, methodology): “In particular, these models are extremely useful for an economic analysis of production or matching functions; see Sperlich, Tjøstheim, and Yang (2002).”
Derivative Estimation and Testing in Generalized Additive Models (2003, unknown): “A detailed introduction, discussion and theory of this method in the context of testing problems combined with marginal integration can be found in Gozalo and Linton (2001), H ardle et al. (2001) or Sperlich et al. (2002).”
Estimation in semiparametric spatial regression (2006, background): “, Yt−k], and sometimes [31] interaction terms are included to improve this approximation.”
Series Estimation of Partially Linear Panel Data Models with Fixed Effects (2002, methodology): “Sperlich et al. (1999) considered the problem of using the kernel method to estimate an additive model with second order interaction terms, but their model does not have a parametric linear component.”
An updated review of Goodness-of-Fit tests for regression models (2013, methodology): “The selection of the function g is also discussed in Stute et al. (2008) with the goal of maximizing power.”
Estimation and Testing for Varying Coefficients in Additive Models With Marginal Integration (2006, methodology): “The reason is that for a test statistic based on kernel-type smoothing, the normal approximation to the distribution of the test statistic is very poor, as shown in Härdle and Mammen (1993) and, more recently, confirmed by Sperlich et al. (2002).”