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- W2070092061 abstract "An important problem for fitting local linear regression is the choice of the smoothing parameter. As the smoothing parameter becomes large, the estimator tends to a straight line, which is the least squares fit in the ordinary linear regression setting. This property may be used to assess the adequacy of a simple linear model. Motivated by Silverman's (1981) work in kernel density estimation, a suitable test statistic is the critical smoothing parameter where the estimate changes from nonlinear to linear, while linearity or non- linearity requires a more precise judgment. We define the critical smoothing parameter through the approximate F-tests by Hastie and Tibshirani (1990). To assess the significance, the “wild bootstrap” procedure is used to replicate the data and the proportion of bootstrap samples which give a nonlinear estimate when using the critical bandwidth is obtained as the p-value. Simulation results show that the critical smoothing test is useful in detecting a wide range of alternatives." @default.
- W2070092061 created "2016-06-24" @default.
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- W2070092061 date "2001-02-01" @default.
- W2070092061 modified "2023-09-25" @default.
- W2070092061 title "Testing the adequacy of a linear modelVIAcritical smoothing" @default.
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- W2070092061 doi "https://doi.org/10.1080/00949650108812070" @default.
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