ADDITIVE COEFFICIENT MODELING VIA POLYNOMIAL SPLINE

A flexible nonparametric regression model is considered in which the response depends linearly on some covariates, with regression coefficients as additive functions of other covariates. Polynomial spline estimators are proposed for the unknown coefficient functions, with optimal univariate mean square convergence rate under geometric mixing condition. Consistent model selection method is also proposed based on a nonparametric Bayes Information Criterion (BIC). Simulated and real data examples demonstrate that the polynomial spline estimators are computationally efficient and also as accurate as the existing local polynomial estimators. Short Running Title. Additive Coefficient Model

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