Maximum Entropy Estimation for Survey sampling

Fabrice Gamboa (1) , Jean-Michel Loubes (1) , Paul Rochet (1)

1 IMT - Institut de Mathématiques de Toulouse UMR5219

Calibration methods have been widely studied in survey sampling over the last decades. Viewing calibration as an inverse problem, we extend the calibration technique by using a maximum entropy method. Finding the optimal weights is achieved by considering random weights and looking for a discrete distribution which maximizes an entropy under the calibration constraint. This method points a new frame for the computation of such estimates and the investigation of its statistical properties.

Mots clés: Survey Sampling; Inverse Problems; Maximum Entropy Method

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Fabrice Gamboa, Jean-Michel Loubes, Paul Rochet. Maximum Entropy Estimation for Survey sampling. 2009. ⟨hal-00419169⟩