Maximum Likelihood with Auxiliary Information
Chambers, Ray and Wang, Suojin (2006) Maximum Likelihood with Auxiliary Information. Southampton, UK, Southampton Statistical Sciences Research Institute, 33pp. (S3RI Methodology Working Papers, (M06/08) ).
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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.
|Item Type:||Monograph (Working Paper)|
|Subjects:||H Social Sciences > HA Statistics|
|Divisions:||University Structure - Pre August 2011 > Southampton Statistical Sciences Research Institute
|Date Deposited:||14 Jun 2006|
|Last Modified:||08 Jun 2012 12:40|
|Contributors:||Chambers, Ray (Author)
Wang, Suojin (Author)
|Date:||14 June 2006|
|Publisher:||Southampton Statistical Sciences Research Institute|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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