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• W2009314534 abstract "Consider the regression model $Y_i = X'_ibeta + g(t_i) + e_i$ for $i = 1, cdots, n$. Here $g$ is an unknown Holder continuous function of known order $p$ in $R, beta$ is a $k times 1$ parameter vector to be estimated and $e_i$ is an unobserved disturbance. Such a model is often encountered in situations in which there is little real knowledge about the nature of $g$. A piecewise polynomial $g_n$ is proposed to approximate $g$. The least-squares estimator $hatbeta$ is obtained based on the model $Y_i = X'_ibeta + g_n(t_i) + e_i$. It is shown that $hatbeta$ can achieve the usual parametric rates $n^{-1/2}$ with the smallest possible asymptotic variance for the case that $X$ and $T$ are correlated." @default.
• W2009314534 created "2016-06-24" @default.
• W2009314534 creator A5073815156 @default.
• W2009314534 date "1988-03-01" @default.
• W2009314534 modified "2023-10-17" @default.
• W2009314534 title "Convergence Rates for Parametric Components in a Partly Linear Model" @default.
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• W2009314534 doi "https://doi.org/10.1214/aos/1176350695" @default.
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