Maximum Likelihood with Auxiliary Information
Maximum Likelihood with Auxiliary Information
Analysis of survey data does not happen in a vacuum. We typically know more about the target population than just the data observed in the survey. In some cases this extra information can be incorporated via calibration of survey weights. However, model fitting using weights often leads to increased standard errors. Also, weights are usually calibrated to a relatively small set of variables, while population data may be known for many more variables. Here we use the general approach to maximum likelihood estimation for complex surveys described in Breckling et al. (1994) to develop methods for efficiently incorporating external population information into model fitting using survey data. In particular, we focus on two simple, but very popular, models fitted to survey data. These are the linear regression model and the logistic regression model.
Southampton Statistical Sciences Research Institute, University of Southampton
Chambers, Ray
96331700-f45e-4483-a887-fef921888ff2
Wang, Suojin
87b7d7d7-ce66-4870-8f0b-d0f809542122
14 June 2006
Chambers, Ray
96331700-f45e-4483-a887-fef921888ff2
Wang, Suojin
87b7d7d7-ce66-4870-8f0b-d0f809542122
Chambers, Ray and Wang, Suojin
(2006)
Maximum Likelihood with Auxiliary Information
(S3RI Methodology Working Papers, M06/08)
Southampton, UK.
Southampton Statistical Sciences Research Institute, University of Southampton
33pp.
Record type:
Monograph
(Working Paper)
Abstract
Analysis of survey data does not happen in a vacuum. We typically know more about the target population than just the data observed in the survey. In some cases this extra information can be incorporated via calibration of survey weights. However, model fitting using weights often leads to increased standard errors. Also, weights are usually calibrated to a relatively small set of variables, while population data may be known for many more variables. Here we use the general approach to maximum likelihood estimation for complex surveys described in Breckling et al. (1994) to develop methods for efficiently incorporating external population information into model fitting using survey data. In particular, we focus on two simple, but very popular, models fitted to survey data. These are the linear regression model and the logistic regression model.
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38977-01.pdf
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Published date: 14 June 2006
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Local EPrints ID: 38977
URI: http://eprints.soton.ac.uk/id/eprint/38977
PURE UUID: 0046af8e-de48-4a7b-ad1a-a79426496496
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Date deposited: 14 Jun 2006
Last modified: 20 Feb 2024 03:21
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Author:
Ray Chambers
Author:
Suojin Wang
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