
doi: 10.2307/1913241
The density of Hermite forms: \[ h(u)=P^ 2_ k(u-\tau)\Phi^ 2(u| \tau,diag(\gamma)) \] where \(P_ k\) is a polynomial of degree K and \(\Phi\) is the density function of the multivariate normal distribution is shown to be capable of approximating any density arbitrarily closely subject to minimal qualifications relating to compactness, denseness, uniform convergence and identification defined over the parameter space.
fitting econometric models, Point estimation, multivariate normal distribution, sample selection, uniform convergence, semi-nonparametric maximum likelihood estimation, Hermite forms, nonlinear regression, semi-parametric, identification, compactness, nonparametric, Nonparametric estimation, Applications of statistics to economics, estimation of Stoker functionals, denseness, Hermite series
fitting econometric models, Point estimation, multivariate normal distribution, sample selection, uniform convergence, semi-nonparametric maximum likelihood estimation, Hermite forms, nonlinear regression, semi-parametric, identification, compactness, nonparametric, Nonparametric estimation, Applications of statistics to economics, estimation of Stoker functionals, denseness, Hermite series
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