showing 5 related works from this author
Efficient Estimation of Non-Linear Finite Population Parameters by Using Non-Parametrics
2013
Summary Currently, high precision estimation of non-linear parameters such as Gini indices, low income proportions or other measures of inequality is particularly crucial. We propose a general class of estimators for such parameters that take into account univariate auxiliary information assumed to be known for every unit in the population. Through a non-parametric model-assisted approach, we construct a unique system of survey weights that can be used to estimate any non-linear parameter that is associated with any study variable of the survey, using a plug-in principle. Based on a rigorous functional approach and a linearization principle, the asymptotic variance of the estimators propose…
Use of functionals in linearization and composite estimation with application to two-sample survey data
2009
An important problem associated with two-sample surveys is the estimation of nonlinear functions of finite population totals such as ratios, correlation coefficients or measures of income inequality. Computation and estimation of the variance of such complex statistics are made more difficult by the existence of overlapping units. In one-sample surveys, the linearization method based on the influence function approach is a powerful tool for variance estimation. We introduce a two-sample linearization technique that can be viewed as a generalization of the one-sample influence function approach. Our technique is based on expressing the parameters of interest as multivariate functionals of fi…
On the usage of joint diagonalization in multivariate statistics
2022
Scatter matrices generalize the covariance matrix and are useful in many multivariate data analysis methods, including well-known principal component analysis (PCA), which is based on the diagonalization of the covariance matrix. The simultaneous diagonalization of two or more scatter matrices goes beyond PCA and is used more and more often. In this paper, we offer an overview of many methods that are based on a joint diagonalization. These methods range from the unsupervised context with invariant coordinate selection and blind source separation, which includes independent component analysis, to the supervised context with discriminant analysis and sliced inverse regression. They also enco…
Estimation de paramètres non linéaires par des méthodes non-paramétriques en population finie
2009
International audience; Nous considérons dans cet article l'estimation de paramètres non-linéaires de totaux en population finie quand une variable auxiliaire est disponible pour chaque individu de la population. Une nouvelle classe d'estimateurs par substitution est obtenue en remplaçant chaque total par un estimateur assisté par un modèle et basé sur une régression non-paramétrique. Pour obtenir la variance asymptotique, la statistique complexe obtenue est ensuite linéarisée par la technique de la fonction d'influence proposée par Deville (1999).
Efficient Estimation of Nonlinear Finite Population Parameters Using Nonparametrics
2012
Currently, the high-precision estimation of nonlinear parameters such as Gini indices, low-income proportions or other measures of inequality is particularly crucial. In the present paper, we propose a general class of estimators for such parameters that take into account univariate auxiliary information assumed to be known for every unit in the population. Through a nonparametric model-assisted approach, we construct a unique system of survey weights that can be used to estimate any nonlinear parameter associated with any study variable of the survey, using a plug-in principle. Based on a rigorous functional approach and a linearization principle, the asymptotic variance of the proposed es…