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Ingrid Van Keilegom
Researcher at Katholieke Universiteit Leuven
Publications - 243
Citations - 5344
Ingrid Van Keilegom is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Estimator & Nonparametric statistics. The author has an hindex of 34, co-authored 221 publications receiving 4744 citations. Previous affiliations of Ingrid Van Keilegom include Pennsylvania State University & Catholic University of Leuven.
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Estimation of semiparametric models when the criterion function is not smooth
TL;DR: In this article, the consistency and asymptotic normality of a class of semiparametric optimization estimators where the criterion function does not obey standard smoothness conditions and simultaneously depends on some preliminary nonparametric estimators are verified.
Report SeriesDOI
Estimation of Semiparametric Models when the Criterion Function Is Not Smooth
TL;DR: In this paper, sufficient conditions for the consistency and asymptotic normality of a class of semiparametric optimization estimators where the criterion function does not obey standard smoothness conditions and simultaneously depends on some nonparametric estimators that can themselves depend on the parameters to be estimated are provided.
Journal ArticleDOI
Extending the Scope of Empirical Likelihood
TL;DR: In this paper, the authors extend the scope of empirical likelihood methodology ill three directions: to allow for plug-in estimates of nuisance parameters in estimating equations, slower than root n-rates of convergence, and settings in which there are a relatively large number of estimating equations compared to the sample size.
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Non-parametric Estimation of the Residual Distribution
TL;DR: In this paper, an estimator of the distribution of e based on non-parametric regression residuals is proposed and its weak convergence is obtained, and applications to prediction intervals and goodness-of-fit tests are discussed.
Posted Content
Estimation of Semiparametric Models When the Criterion Function is Not Smooth
TL;DR: In this paper, sufficient conditions for the consistency and asymptotic normality of a class of semiparametric optimization estimators where the criterion function does not obey standard smoothness conditions and simultaneously depends on some nonparametric estimators that can themselves depend on the parameters to be estimated are provided.