Statistical inference for the index parameter in single-index models
In this paper, we are concerned with statistical inference for the index parameter in the single-index model . Based on the estimates obtained by the local linear method, we extend the generalized likelihood ratio test to the single-index model. We investigate the asymptotic behaviour of the proposed test and demonstrate that its limiting null distribution follows a [chi]2-distribution, with the scale constant and the number of degrees of freedom being independent of nuisance parameters or functions, which is called the Wilks phenomenon. A simulated example is used to illustrate the performance of the testing approach.
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Volume (Year): 101 (2010)
Issue (Month): 4 (April)
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- Hardle, W. & Tsybakov, A., 1991.
"How sensitive are average derivates ?,"
CORE Discussion Papers
1991044, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Fan, Jianqing & Jiang, Jiancheng, 2005. "Nonparametric Inferences for Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 890-907, September.
- Jianqing Fan, 2004. "Generalised likelihood ratio tests for spectral density," Biometrika, Biometrika Trust, vol. 91(1), pages 195-209, March.
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