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Empirical likelihood based inference under complex sampling designs

Berger, Yves G. (2015) Empirical likelihood based inference under complex sampling designs At Journées de Méthodologie Statistique. 8 pp.

Record type: Conference or Workshop Item (Paper)


The approach proposed gives design-consistent estimators of parameters which are solutions of estimating equations (e.g. averages, totals, quantiles, correlation, (non)linear regression parameters). It can be used to construct confidence intervals without variance estimates. These confidence intervals are not based on the normality of the point estimator. Linearisation, re-sampling (jackknife or bootstrap) or joint-inclusion probabilities are not necessary, even when the parameter of interest is not linear. This approach gives consistent confidence intervals even when the sampling distribution is skewed (e.g. with domains or with outlying values), or when linearisation gives biased variance estimates. The proposed approach can be used to estimate generalised regression parameters (e.g. logistic regression) and to test if they are significant, under a design-based approach. The auxiliary information is naturally taken into account, without the need of a calibration distance function. The empirical likelihood approach is a design-based approach. A super-population model is not necessary. The empirical likelihood approach proposed is different from the pseudoempirical likelihood approach

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Published date: March 2015
Venue - Dates: Journées de Méthodologie Statistique, 2015-03-01
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Organisations: Statistical Sciences Research Institute


Local EPrints ID: 375514
PURE UUID: 0f204a21-569f-4f51-bede-4fb83dd08a62

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Date deposited: 02 Apr 2015 10:30
Last modified: 17 Jul 2017 21:16

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Author: Yves G. Berger

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