<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Two sets of diagnostics are presented to evaluate the properties of generalized variance functions (GVFs) for a given sample survey. The first set uses test statistics for the coefficients of multiple regression forms of GVF models. The second set uses smoothed estimators of the mean squared error (MSE) of GVF-based variance estimators. The smooth version of the MSE estimator can provide a useful measure of the performance of a GVF estimator, relative to the variance of a standard design-based variance estimator. Some of the proposed methods are applied to sample data from the Current Employment Statistics survey.</p></abstract>

Two sets of diagnostics are presented to evaluate the properties of generalized variance functions (GVFs) for a given sample survey. The first set uses test statistics for the coefficients of multiple regression forms of GVF models. The second set uses smoothed estimators of the mean squared error (MSE) of GVF-based variance estimators. The smooth version of the MSE estimator can provide a useful measure of the performance of a GVF estimator, relative to the variance of a standard design-based variance estimator. Some of the proposed methods are applied to sample data from the Current Employment Statistics survey.

Evaluation of Generalized Variance Functions in the Analysis of Complex Survey Data

U.S. Bureau of Labor Statistics, 2 Massachusetts Ave. N.E., Washington, DC 20212, U.S.A

Journal of Official Statistics

Two sets of diagnostics are presented to evaluate the properties of generalized variance functions (GVFs) for a given sample survey. The first set uses test...

Evaluation of Generalized Variance Functions in the Analysis of Complex Survey Data

Online veröffentlicht: 14. Feb. 2014

Seitenbereich: 63 - 90

DOI: https://doi.org/10.2478/jos-2014-0004

Schlüsselwörter
Complex sample design, degrees of freedom, design-based inference, model-based inference, quarterly census of employment and wages, superpopulation model, U.S. current employment statistics (CES) survey, variance estimator stability

This content is open access.

Evaluation of Generalized Variance Functions in the Analysis of Complex Survey Data

Online veröffentlicht: 14. Feb. 2014

Seitenbereich: 63 - 90

DOI: https://doi.org/10.2478/jos-2014-0004

SchlüsselwörterComplex sample design, degrees of freedom, design-based inference, model-based inference, quarterly census of employment and wages, superpopulation model, U.S. current employment statistics (CES) survey, variance estimator stability

This content is open access.

Schlüsselwörter
Complex sample design, degrees of freedom, design-based inference, model-based inference, quarterly census of employment and wages, superpopulation model, U.S. current employment statistics (CES) survey, variance estimator stability