No abstract

This paper is concerned with the asymptotic properties of the converged solution of the raking-ratio estimator. After a description of the raking-ratio estimation procedure, we show that the estimator can be unbiased in the presence of nonresponse if the nonresponse has a particular structure. We also give the asymptotic variance of the estimator. Finally, the results of two numerical applications are presented. RESUMECet article s'intkresse aux propriktks asymptotiques de la solution aprks convergence de l'estimateur par le quotient obtenu par ratissage. Aprb avoir dkcrit la prockdure de ratissage et l'estimateur qui en decode, nous montrons que ce dernier peut &tre sans biais en prksence de non-rkponse si la non-rkponse admet une structure particulihe. Nous fournissons kgalement la variance asymptotique de l'estimateur. Finalement, nous prksentons les rksultats de deux applications numkriques.

Generalized regression (GREG) estimation uses a model that assumes that the values of the variable of interest are not correlated. An extension of the GREG estimator to the case where the vector of interest has a positive definite covariance structure is presented in this article. This extension can be translated to the calibration estimators. The key to this extension lies in a generalization of the Horvitz-Thompson estimator which, in some sense, also assumes that the values of the variable of interest are not correlated. The Godambe-Joshi lower bound is another result which assumes a model with no correlation. This is also generalized to a vector of interest with a positive definite covariance structure, and it is shown that the generalized calibration estimator asymptotically attains this generalized lower bound. Properties of the new estimators are given, and they are compared with the Horvitz-Thompson estimator and the usual calibration estimator. The new estimators are applied to the Canadian Reverse Record Check survey and to the problem of variance estimation.