Abstract:
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The linear model with a growing number of predictors arises in many contemporary scientific endeavor. In this presentation, we consider the commonly used ridge estimator in partial linear models when a strictly linear model is inadequate given that some of the relations are believed to be of certain linear form while others are not easily parameterized, and thus a semiparametric partial linear model is considered. For these semiparametric partial linear models with p is bigger than n, we develop a procedure to estimate the linear coefficients as if the nonparametric part is not present. The properties of the proposed estimator for the linear component is studied for growing p. Data analysis is presented to support that the proposed estimator of the linear component performs very well.
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