Empirical likelihood confidence intervals and significance test for regression parameters under complex sampling designs
Empirical likelihood confidence intervals and significance test for regression parameters under complex sampling designs
Confidence intervals based on ordinary least squares may have poor coverages for regression parameters when the effect of sampling design is ignored. Standard confidence intervals based on design variances may not have the right coverages when the sampling distribution is skewed. Berger and De La Riva Torres (2012) proposed an empirical likelihood approach which can be used for point estimation and to construct confidence intervals under complex sampling designs for a single parameter. We show that this approach can be extended to test the significance of a subset of model parameters and to derive confidence intervals. The proposed approach is not a straightforward extension of Berger and De La Riva Torres (2012) approach, because we consider the situation when the parameter is multidimensional and the parameter of interest is a subset of the parameter. This requires profiling which is not covered by Berger and De La Riva Torres (2012). The proposed approach intrinsically incorporates sampling weights, design variables, and auxiliary information. It may yield to more accurate confidence intervals when the sampling distribution of the regression parameters is not normal, the point estimator is biased, or the regression model is not linear. The proposed approach is simple to implement and less computer intensive than bootstrap. The proposed approach does not rely on re-sampling, linearisation, variance estimation, or design-effect.
Design-based inference, estimating equations, empirical likelihood, regression parameters, unequal inclusion probabilities
2070-2079
Oguz Alper, Melike
02d5ed8a-e9e3-438a-95c0-709acd83a5f8
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Oguz Alper, Melike
02d5ed8a-e9e3-438a-95c0-709acd83a5f8
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Oguz Alper, Melike and Berger, Yves G.
(2014)
Empirical likelihood confidence intervals and significance test for regression parameters under complex sampling designs.
JSM 2014 - Joint Statistical Meetings - American Statistical Association, Boston, United States.
02 - 07 Aug 2014.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Confidence intervals based on ordinary least squares may have poor coverages for regression parameters when the effect of sampling design is ignored. Standard confidence intervals based on design variances may not have the right coverages when the sampling distribution is skewed. Berger and De La Riva Torres (2012) proposed an empirical likelihood approach which can be used for point estimation and to construct confidence intervals under complex sampling designs for a single parameter. We show that this approach can be extended to test the significance of a subset of model parameters and to derive confidence intervals. The proposed approach is not a straightforward extension of Berger and De La Riva Torres (2012) approach, because we consider the situation when the parameter is multidimensional and the parameter of interest is a subset of the parameter. This requires profiling which is not covered by Berger and De La Riva Torres (2012). The proposed approach intrinsically incorporates sampling weights, design variables, and auxiliary information. It may yield to more accurate confidence intervals when the sampling distribution of the regression parameters is not normal, the point estimator is biased, or the regression model is not linear. The proposed approach is simple to implement and less computer intensive than bootstrap. The proposed approach does not rely on re-sampling, linearisation, variance estimation, or design-effect.
Text
312034_88596.pdf
- Author's Original
More information
e-pub ahead of print date: August 2014
Venue - Dates:
JSM 2014 - Joint Statistical Meetings - American Statistical Association, Boston, United States, 2014-08-02 - 2014-08-07
Keywords:
Design-based inference, estimating equations, empirical likelihood, regression parameters, unequal inclusion probabilities
Organisations:
Statistical Sciences Research Institute
Identifiers
Local EPrints ID: 377868
URI: http://eprints.soton.ac.uk/id/eprint/377868
PURE UUID: 7ac08cee-5407-403a-8578-4614fc3260d3
Catalogue record
Date deposited: 24 Jun 2015 12:03
Last modified: 15 Mar 2024 04:14
Export record
Contributors
Author:
Melike Oguz Alper
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics