Author
Wenceslao González-Manteiga
Other affiliations: Lancaster University, Charles III University of Madrid, University of Santiago, Chile ...read more
Bio: Wenceslao González-Manteiga is an academic researcher from University of Santiago de Compostela. The author has contributed to research in topics: Estimator & Regression analysis. The author has an hindex of 29, co-authored 165 publications receiving 2927 citations. Previous affiliations of Wenceslao González-Manteiga include Lancaster University & Charles III University of Madrid.
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the authors proposed a method based on functional depths to detect outliers in functional data. But the performance of the proposed procedure is analyzed by several Monte Carlo experiments and they illustrate the procedure by finding outliers by using a dataset of NOx emissions taken from a control station near an industrial area.
Abstract: This paper analyzes outlier detection for functional data by means of functional depths, which measures the centrality of a given curve within a group of trajectories providing center-outward orderings of the set of curves. We give some insights of the usefulness of looking for outliers in functional datasets and propose a method based in depths for the functional outlier detection. The performance of the proposed procedure is analyzed by several Monte Carlo experiments. Finally, we illustrate the procedure by finding outliers in a dataset of NOx (nitrogen oxides) emissions taken from a control station near an industrial area. Copyright © 2007 John Wiley & Sons, Ltd.
228 citations
TL;DR: In this article, the authors present a survey of the developments on Goodness-of-Fit for regression models during the last 20 years, from the very first origins with the idea of the tests for density and distribution, until the most recent advances for complex data and models.
Abstract: This survey intends to collect the developments on Goodness-of-Fit for regression models during the last 20 years, from the very first origins with the proposals based on the idea of the tests for density and distribution, until the most recent advances for complex data and models. Far from being exhaustive, the contents in this paper are focused on two main classes of tests statistics: smoothing-based tests (kernel-based) and tests based on empirical regression processes, although other tests based on Maximum Likelihood ideas will be also considered. Starting from the simplest case of testing a parametric family for the regression curves, the contributions in this field provide also testing procedures in semiparametric, nonparametric, and functional models, dealing also with more complex settings, as those ones involving dependent or incomplete data.
161 citations
TL;DR: A generalized Kaplan-Meier estimator has been considered in the literature on conditional survival analysis (Beran (1981), Gonzalez-Manteiga and Cadarso-Suarez (1991) and Gentleman and Crowley (1991)).
Abstract: A generalized Kaplan-Meier estimator has been considered in the literature on conditional survival analysis (Beran (1981), Gonzalez-Manteiga and Cadarso-Suarez (1991) and Gentleman and Crowley (1991)). An almost sure representation as a sum of independent variables is given here for this estimator. Some applications are obtained as consequences of these results.
126 citations
TL;DR: In this paper, a bootstrap procedure is proposed for estimating the mean squared error (MSE) of the EBLUP under the finite population setup, and a simulation experiment is carried out in order to compare the performance of two different bootstrap estimators with the approximation given by Prasad and Rao.
Abstract: Concerning the estimation of linear parameters in small areas, a nested-error regression model is assumed for the values of the target variable in the units of a finite population. Then, a bootstrap procedure is proposed for estimating the mean squared error (MSE) of the EBLUP under the finite population setup. The consistency of the bootstrap procedure is studied, and a simulation experiment is carried out in order to compare the performance of two different bootstrap estimators with the approximation given by Prasad and Rao [Prasad, N.G.N. and Rao, J.N.K., 1990, The estimation of the mean squared error of small-area estimators. Journal of the American Statistical Association, 85, 163–171.]. In the numerical results, one of the bootstrap estimators shows a better bias behavior than the Prasad–Rao approximation for some of the small areas and not much worse in any case. Further, it shows less MSE in situations of moderate heteroscedasticity and under mispecification of the error distribution as normal whe...
117 citations
TL;DR: Under a logistic mixed linear model for the characteristic of interest, the Prasad-Rao-type formula is compared with a bootstrap estimator obtained by a wild bootstrap designed for estimating under finite populations.
103 citations
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6,278 citations
TL;DR: Convergence of Probability Measures as mentioned in this paper is a well-known convergence of probability measures. But it does not consider the relationship between probability measures and the probability distribution of probabilities.
Abstract: Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.
5,689 citations
TL;DR: It is concluded that multiple Imputation for Nonresponse in Surveys should be considered as a legitimate method for answering the question of why people do not respond to survey questions.
Abstract: 25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.
3,216 citations
3,152 citations