Influence diagnostics in regression with complex designs through conditional bias

One of the areas of Statistics in which the influence analysis has been widely studied is the multiple linear regression model. Nevertheless, the influence diagnostics proposed in this context cannot be applied to regression in complex survey, under randomized inference, since the i.i.d. case does n...

Full description

Saved in:
Bibliographic Details
Published in:Test (Madrid, Spain) Spain), 2005-12, Vol.14 (2), p.515-542
Main Authors: Jiménez-Gamero, M. Dolores, Moreno-Rebollo, Juan Luis, Muñoz-Pichardo, Juan M., Muñoz-Reyes, Ana M.
Format: Article
Language:English
Subjects:
Conditional bias
design-based regression
influential observation
Regression analysis
Studies
survey sampling
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:One of the areas of Statistics in which the influence analysis has been widely studied is the multiple linear regression model. Nevertheless, the influence diagnostics proposed in this context cannot be applied to regression in complex survey, under randomized inference, since the i.i.d. case does not incorporate any probability weighting or population structure, such as clustering, stratification or measures of size into the analysis. In this paper we introduce some influence diagnostics in regression in complex survey. They are built on the conditional bias concept (Moreno-Rebollo et al., 1999). We emphasize the similarities and differences of the proposed measures with respect to the existing ones for the i.i.d. case.[PUBLICATION ABSTRACT]
ISSN:1133-0686
1863-8260
DOI:10.1007/BF02595416