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Density estimation by neural networks
This dissertation develops density estimation and filtering methods to analyze complex high-frequency data prone to uncertainty pronounced in missing observations or errors by measurement
Consistent estimates for categorical data based on a mix of administrative data sources and surveys
Dissertation on multiple imputation of latent classes to simultaneously estimate and correct for misclassification and missing data in combined datasets.
Variances of Census Tables after Mass Imputation
We consider variance estimation for the Dutch virtual Census, when mass imputation is used for educational attainment.
A MIP approach for a generalised data editing problem
A mixed-integer programming formulation is presented of automatic error localisation with general edit operations.
Mass Imputation for Census Estimation
Estimating educational attainment levels for the Dutch Virtual Census
Imputation of Numerical Data under Edit Restrictions
This paper discusses a new imputation method for estimating missing data.
Estimating Classification Error under Edit Restrictions in Combined Survey-Register Data
This discussion paper describes a method based on latent class modeling
An application of population size estimation to offcial statistics: Sensitivity of model assumptions and the effect of implied coverage
Dissertation on estimating the undercoverage of a population register, and the effects of violating the assumptions of capture-recapture methods and missing values.
Restrictive imputation of incomplete survey data
Dissertation G. Vink, Restrictive imputation of incomplete survey data, Missing data, imputation, multiple imputation, estimation methods under restrictions
Imputation of restricted data: Applications to business suveys
Dissertation on models developed to impute business data, many of which are subject to linear equality and inequality constraints.