In this paper, we consider the application of the empirical likelihood method to partially linear model. Unlike the usual cases, we first propose an approximation to the residual of the model to deal with the nonparametric part so that Owen's (1990) empirical likelihood approach can be applied. Then
Empirical likelihood method for linear transformation models
โ Scribed by Wen Yu; Yunting Sun; Ming Zheng
- Book ID
- 106340887
- Publisher
- Springer Japan
- Year
- 2009
- Tongue
- English
- Weight
- 218 KB
- Volume
- 63
- Category
- Article
- ISSN
- 0020-3157
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๐ SIMILAR VOLUMES
Consider the partial linear model Y i =X { i ;+ g(T i )+= i , i=1, ..., n, where ; is a p\_1 unknown parameter vector, g is an unknown function, X i 's are p\_1 observable covariates, T i 's are other observable covariates in [0, 1], and Y i 's are the response variables. In this paper, we shall con
The empirical likelihood method of Owen [Owen, A., 1988. Empirical likelihood ratio confidence intervals for single functional. Biometrika 75, 237-249], is extended to partial linear models with fixed designs in this paper. A nonparametric version of Wilks' theorem is derived. The result is then use