<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></abstract>

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.

Estimation when the Covariance Structure of the Variable of Interest is Positive Definite

1221 du Gouverneur, Ottawa, Ontario K4C 1E2,

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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...

Estimation when the Covariance Structure of the Variable of Interest is Positive Definite

Publié en ligne: 21 févr. 2017

Pages: 275 - 299

Reçu: 01 juil. 2015

Accepté: 01 juil. 2016

DOI: https://doi.org/10.1515/jos-2017-0014

Mots clés
Asymptotic setup, calibration estimators, Godambe-Joshi lower bound, Horvitz- Thompson estimator, Moore-Penrose inverse

© by Alain Théberge

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Estimation when the Covariance Structure of the Variable of Interest is Positive Definite

Publié en ligne: 21 févr. 2017

Pages: 275 - 299

Reçu: 01 juil. 2015

Accepté: 01 juil. 2016

DOI: https://doi.org/10.1515/jos-2017-0014

Mots clésAsymptotic setup, calibration estimators, Godambe-Joshi lower bound, Horvitz- Thompson estimator, Moore-Penrose inverse

© by Alain Théberge

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
Asymptotic setup, calibration estimators, Godambe-Joshi lower bound, Horvitz- Thompson estimator, Moore-Penrose inverse