Correcting for misclassification under edit restrictions in combined survey-register data using Multiple Imputation Latent Class modelling (MILC)

L. Boeschoten

Research output: Contribution to conferencePaperOther research output

Conference

ConferenceUN/ECE Work Session on Statistical Data Editing 2017: New perspectives for data editing in the context of new data sources and data integration. New and emerging methods
Period24/04/17 → …

Cite this

Boeschoten, L. (2017). Correcting for misclassification under edit restrictions in combined survey-register data using Multiple Imputation Latent Class modelling (MILC). Paper presented at UN/ECE Work Session on Statistical Data Editing 2017: New perspectives for data editing in the context of new data sources and data integration. New and emerging methods , .
Boeschoten, L. / Correcting for misclassification under edit restrictions in combined survey-register data using Multiple Imputation Latent Class modelling (MILC). Paper presented at UN/ECE Work Session on Statistical Data Editing 2017: New perspectives for data editing in the context of new data sources and data integration. New and emerging methods , .
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title = "Correcting for misclassification under edit restrictions in combined survey-register data using Multiple Imputation Latent Class modelling (MILC)",
author = "L. Boeschoten",
year = "2017",
language = "English",
note = "UN/ECE Work Session on Statistical Data Editing 2017: New perspectives for data editing in the context of new data sources and data integration. New and emerging methods ; Conference date: 24-04-2017",

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Boeschoten, L 2017, 'Correcting for misclassification under edit restrictions in combined survey-register data using Multiple Imputation Latent Class modelling (MILC)' Paper presented at UN/ECE Work Session on Statistical Data Editing 2017: New perspectives for data editing in the context of new data sources and data integration. New and emerging methods , 24/04/17, .

Correcting for misclassification under edit restrictions in combined survey-register data using Multiple Imputation Latent Class modelling (MILC). / Boeschoten, L.

2017. Paper presented at UN/ECE Work Session on Statistical Data Editing 2017: New perspectives for data editing in the context of new data sources and data integration. New and emerging methods , .

Research output: Contribution to conferencePaperOther research output

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T1 - Correcting for misclassification under edit restrictions in combined survey-register data using Multiple Imputation Latent Class modelling (MILC)

AU - Boeschoten, L.

PY - 2017

Y1 - 2017

M3 - Paper

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Boeschoten L. Correcting for misclassification under edit restrictions in combined survey-register data using Multiple Imputation Latent Class modelling (MILC). 2017. Paper presented at UN/ECE Work Session on Statistical Data Editing 2017: New perspectives for data editing in the context of new data sources and data integration. New and emerging methods , .