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 for partial linear models with fixed designs
โ Scribed by Qi-Hua Wang; Bing-Yi Jing
- Book ID
- 104303011
- Publisher
- Elsevier Science
- Year
- 1999
- Tongue
- English
- Weight
- 378 KB
- Volume
- 41
- Category
- Article
- ISSN
- 0167-7152
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โฆ Synopsis
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 used to construct confidence regions of the parameter vector in the partial linear models with asymptotically correct coverage probabilities.
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