A difference based approach to the semiparametric partial linear model

A commonly used semiparametric partial linear model is con- sidered. We propose analyzing this model using a difference based approach. The procedure estimates the linear component based on the differences of the observations and then estimates the nonparametric component by ei- ther a kernel or a wavelet thresholding method using the residuals of the linear fit. It is shown that both the estimator of the linear component and the estimator of the nonparametric component asymptotically perform as well as if the other component were known. The estimator of the linear com- ponent is asymptotically efficient and the estimator of the nonparametric component is asymptotically rate optimal. A test for linear combinations of the regression coefficients of the linear component is also developed. Both the estimation and the testing procedures are easily implementable. Nu- merical performance of the procedure is studied using both simulated and real data. In particular, we demonstrate our method in an analysis of an attitude data set as well as a data set from the Framingham Heart Study.

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