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

L. Boeschoten

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

L. Boeschoten

Department of Methodology and Statistics

Research output: Contribution to conference › Paper › Other research output

Original language	English
Publication status	Published - 2017
Event	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 - The Hague, Netherlands Duration: 24 Apr 2017 → …

Conference

Conference	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
Country/Territory	Netherlands
City	The Hague
Period	24/04/17 → …

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 , The Hague, Netherlands.

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 , The Hague, Netherlands.

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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 , The Hague, Netherlands, 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 , The Hague, Netherlands.

Research output: Contribution to conference › Paper › Other research output

TY - CONF

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

T2 - 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

Y2 - 24 April 2017

ER -

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 , The Hague, Netherlands.