Anne Ruiz-Gazen from the Toulouse School of Economics will give a guest lecture on Tuesday 26.08 at 10 am in the seminar room 469.

Title: Taking into account auxiliary information for complex finite population
parameters estimation in surveys.

Speaker: Anne Ruiz-Gazen, Toulouse School of Economics, Université Toulouse 1 Capitole France. Joint work with Camelia Goga, Institut de Mathématiques de Bourgogne.

Summary: Precise estimation of complex parameters such as Gini indices or other inequality measures is currently a problem of interest. In order to improve on the precision of existing estimators, we propose a general class of estimators for complex parameters which allows to take into account univariate auxiliary information assumed to be known for each unit in the population. We will begin the presentation by some basics on estimation in survey sampling theory with and without auxiliary information when the inference is based on the design. Model-assisted and calibration approaches will be detailed in a simple framework. Then, we will present our new approach which is a nonparametric model-assisted method. Its advantage is that it leads to a unique sampling weights system for any complex parameter and any variable of interest. The asymptotic properties of this class of estimators will be given in the particular case of B-splines nonparametric estimators. Simulations using data from the French Employment survey will illustrate the good properties of our proposal. Some comparisons with existing methods will also be presented.

Anne Ruiz-Gazen from the Toulouse School of Economics will give a guest lecture on Tuesday 26.08 at 10 am in the seminar room 469.

Title: Taking into account auxiliary information for complex finite population
parameters estimation in surveys.

Speaker: Anne Ruiz-Gazen, Toulouse School of Economics, Université Toulouse 1 Capitole France. Joint work with Camelia Goga, Institut de Mathématiques de Bourgogne.

Summary: Precise estimation of complex parameters such as Gini indices or other inequality measures is currently a problem of interest. In order to improve on the precision of existing estimators, we propose a general class of estimators for complex parameters which allows to take into account univariate auxiliary information assumed to be known for each unit in the population. We will begin the presentation by some basics on estimation in survey sampling theory with and without auxiliary information when the inference is based on the design. Model-assisted and calibration approaches will be detailed in a simple framework. Then, we will present our new approach which is a nonparametric model-assisted method. Its advantage is that it leads to a unique sampling weights system for any complex parameter and any variable of interest. The asymptotic properties of this class of estimators will be given in the particular case of B-splines nonparametric estimators. Simulations using data from the French Employment survey will illustrate the good properties of our proposal. Some comparisons with existing methods will also be presented.