Add to ORKGLarge complex datasets typically contain large numbers of variables measured on even larger numbers of respondents. Such datasets are the logical result of surveys that attempt to understand th
Several statistical agencies use, or are considering the use of, multiple imputation to limit the ri...
Multiple imputation is an effectivemethod for dealing with missing data, and it is becoming increasi...
Kleinke KM, Reinecke J. Multiple Imputation of Multilevel Count Data. In: Engel U, Jann B, Lynn P, S...
this paper, we provide more detail on the algorithm than has previously been given and present some ...
This dissertation focuses on finding plausible imputations when there is some restriction posed on t...
Multiple data sources are becoming increasingly available for statistical analyses in the era of big...
When faced with missing data in a statistical survey or administrative sources, imputation is freque...
This paper deals with imputation techniques and strategies. Usually, imputation truly commences afte...
Most of the research work on imputation has concentrated on improving methods of imputing missing va...
Background: Missing data in a large scale survey presents major challenges. We focus on performing m...
Philosophiae Doctor - PhD (Statistics and Population Studies)The aim of this study is to look at the...
Missing data are often a problem in social science data. Imputation methods fill in the missing resp...
Often, analyzing administrative data we have a large number of units and variables and many missing ...
Imputation is a method of adjusting for missing data. Missing responses to data items is a common pr...
In practical survey sampling, missing data are unavoidable due to nonresponse, rejected observations...
Several statistical agencies use, or are considering the use of, multiple imputation to limit the ri...
Multiple imputation is an effectivemethod for dealing with missing data, and it is becoming increasi...
Kleinke KM, Reinecke J. Multiple Imputation of Multilevel Count Data. In: Engel U, Jann B, Lynn P, S...
this paper, we provide more detail on the algorithm than has previously been given and present some ...
This dissertation focuses on finding plausible imputations when there is some restriction posed on t...
Multiple data sources are becoming increasingly available for statistical analyses in the era of big...
When faced with missing data in a statistical survey or administrative sources, imputation is freque...
This paper deals with imputation techniques and strategies. Usually, imputation truly commences afte...
Most of the research work on imputation has concentrated on improving methods of imputing missing va...
Background: Missing data in a large scale survey presents major challenges. We focus on performing m...
Philosophiae Doctor - PhD (Statistics and Population Studies)The aim of this study is to look at the...
Missing data are often a problem in social science data. Imputation methods fill in the missing resp...
Often, analyzing administrative data we have a large number of units and variables and many missing ...
Imputation is a method of adjusting for missing data. Missing responses to data items is a common pr...
In practical survey sampling, missing data are unavoidable due to nonresponse, rejected observations...
Several statistical agencies use, or are considering the use of, multiple imputation to limit the ri...
Multiple imputation is an effectivemethod for dealing with missing data, and it is becoming increasi...
Kleinke KM, Reinecke J. Multiple Imputation of Multilevel Count Data. In: Engel U, Jann B, Lynn P, S...