42 citations · 86 across the 9 of their papers we have counts for
11 papers
Model-assisted estimation in high-dimensional settings for survey data
Mehdi Dagdoug, Camelia Goga, David Haziza
Model-assisted estimators have attracted a lot of attention in the last three decades. These estimators attempt to make an efficient use of auxiliary information available at the e…
Imputation procedures in surveys using nonparametric and machine learning methods: an empirical comparison
Mehdi Dagdoug, Camelia Goga, David Haziza
Nonparametric and machine learning methods are flexible methods for obtaining accurate predictions. Nowadays, data sets with a large number of predictors and complex structures are…
Model-assisted estimation through random forests in finite population sampling
Mehdi Dagdoug, Camelia Goga, David Haziza
In surveys, the interest lies in estimating finite population parameters such as population totals and means. In most surveys, some auxiliary information is available at the estima…
Conditional bias robust estimation of the total of curve data by sampling in a finite population: an illustration on electricity load curves
Hervé Cardot, Anne De Moliner Anne, Camelia Goga
For marketing or power grid management purposes, many studies based on the analysis of the total electricity consumption curves of groups of customers are now carried out by electr…
Estimating with kernel smoothers the mean of functional data in a finite population setting. A note on variance estimation in presence of partially observed trajectories
Hervé Cardot, Anne De Moliner, Camelia Goga
In the near future, millions of load curves measuring the electricity consumption of French households in small time grids (probably half hours) will be available. All these collec…
Improving the estimation of the odds-ratio using auxiliary information
C. Goga, A Ruiz-Gazen
The odds ratio measure is used in health and social surveys where the odds of a certain event is to be compared between two populations. It is defined using logistic regression, an…
Variance estimation and asymptotic confidence bands for the mean estimator of sampled functional data with high entropy unequal probability sampling designs
Hervé Cardot, Camelia Goga, Pauline Lardin
For fixed size sampling designs with high entropy it is well known that the variance of the Horvitz-Thompson estimator can be approximated by the Hájek formula. The interest of thi…
Uniform convergence and asymptotic confidence bands for model-assisted estimators of the mean of sampled functional data
Hervé Cardot, Camelia Goga, Pauline Lardin
When the study variable is functional and storage capacities are limited or transmission costs are high, selecting with survey sampling techniques a small fraction of the observati…
Using complex surveys to estimate the -median of a functional variable: application to electricity load curves
Mohamed Chaouch, Camelia Goga
Mean profiles are widely used as indicators of the electricity consumption habits of customers. Currently, in Électricité De France (EDF), class load profiles are estimated using p…
Efficient Estimation of Nonlinear Finite Population Parameters Using Nonparametrics
Camelia Goga, Anne Ruiz-Gazen
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 presen…
Properties of Design-Based Functional Principal Components Analysis
Hervé Cardot, Mohamed Chaouch, Camelia Goga +1
This work aims at performing Functional Principal Components Analysis (FPCA) with Horvitz-Thompson estimators when the observations are curves collected with survey sampling techni…