Likelihood-Based Local Polynomial Fitting for Single-Index Models
The parametric generalized linear model assumes that the conditional distribution of a response Y given a d-dimensional covariate X belongs to an exponential family and that a known transformation of the regression function is linear in X. In this paper we relax the latter assumption by considering a nonparametric function of the linear combination [beta]TX, say [eta]0([beta]TX). To estimate the coefficient vector [beta] and the nonparametric component [eta]0 we consider local polynomial fits based on kernel weighted conditional likelihoods. We then obtain an estimator of the regression function by simply replacing [beta] and [eta]0 in [eta]0([beta]TX) by these estimators. We derive the asymptotic distributions of these estimators and give the results of some numerical experiments.
Volume (Year): 80 (2002)
Issue (Month): 2 (February)
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References listed on IDEAS
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- Pollard, David, 1991. "Asymptotics for Least Absolute Deviation Regression Estimators," Econometric Theory, Cambridge University Press, vol. 7(02), pages 186-199, June.
- Powell, James L & Stock, James H & Stoker, Thomas M, 1989. "Semiparametric Estimation of Index Coefficients," Econometrica, Econometric Society, vol. 57(6), pages 1403-30, November.
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