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Econometrica
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Econometrica
Article . 1987 . Peer-reviewed
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Semi-Nonparametric Maximum Likelihood Estimation

Semi-nonparametric maximum likelihood estimation
Authors: Gallant, A Ronald; Nychka, Douglas W;

Semi-Nonparametric Maximum Likelihood Estimation

Abstract

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.

Keywords

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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    548
    popularity
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    Top 1%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
548
Top 1%
Top 0.1%
Top 10%
bronze