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Generalised Variance Function Estimation for Binary Variables in Large‐Scale Sample Surveys
Author(s) -
Cao Ricardo,
Vilar José A.,
Vilar Juan M.
Publication year - 2012
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/j.1467-842x.2012.00682.x
Subject(s) - heteroscedasticity , estimator , mathematics , statistics , outlier , variance (accounting) , nonparametric statistics , small area estimation , econometrics , estimation , parametric statistics , scale (ratio) , sample (material) , geography , accounting , management , cartography , economics , business , chemistry , chromatography
Summary Generalised variance function (GVF) models are data analysis techniques often used in large‐scale sample surveys to approximate the design variance of point estimators for population means and proportions. Some potential advantages of the GVF approach include operational simplicity, more stable sampling errors estimates and providing a convenient method of summarising results when a high number of survey variables is considered. In this paper, several parametric and nonparametric methods for GVF estimation with binary variables are proposed and compared. The behavior of these estimators is analysed under heteroscedasticity and in the presence of outliers and influential observations. An empirical study based on the annual survey of living conditions in Galicia (a region in the northwest of Spain) illustrates the behaviour of the proposed estimators.