Translation of "Multiple imputation" in German

Results of a sensitivity analysis using placebo based multiple imputation (PMI) were consistent with the primary analysis.

Furthermore, to our knowledge no software is currently available for dealing with measurement error using multiple imputation approaches.

It offers multiple imputation routines in hierarchical settings form any variable types (for example, nominal, ordinal, or continuous variables).

In the context of hierarchical data, multiple imputation is associated with special challenges, because the correct specification of the imputation model requires not only the data structure, but also the planned analysis.

We have analyzed the available database with several methods, like Small Area Estimation, Multiple Imputation and regressions.

Finally, an overview about recently available multiple imputation software is given and violations of the assumptions made are addressed."

Guidelines for the use of the multiple imputation method, based on currently available software and on functions and modules to be developed (callable in R), will be published, particularly with regard to possible limitations discussed in the literature.

This includes the procedure of multiple imputation, with the help of which several plausible "substitutions" for all missing values are generated on the basis of (a) the observed values and (b) a statistical model (the imputation model).

The user-friendly setup which only requires the data and optionally the specification of the analysis model of interest makes the package especially attractive for users less familiar with the peculiarities of multiple imputation.

It is shown that standard multiple imputation techniques can be used to estimate sophisticated econometric models from large-scale panel data exposed to item-nonresponse.

In this context, he has also written a tutorial that is intended to give applied scientists an introduction to the use of multiple imputation for hierarchical data.

In Section III we briefly discuss the two main approaches for multiple imputation, joint modeling and sequential regression, and discuss their advantages and disadvantages.

The problem at hand is: If the statistical model incorrectly specified, then the multiple imputation will also result in distortions.

The multiple imputation procedure constitutes a general approach to handling missing data, in which different plausible substitutions for missing values can be generated based on a statistical models.

Multiple imputation, raking, and calibration techniques are compared for estimating the number of unemployed in different settings.

After proper multiple imputation of the missing values - thus avoiding obvious nonresponse bias -, the results on the collective bargaining effects on productivity and efficiency change significantly.

In this paper we introduce two approaches based on multiple imputation and their possible application to the German IAB Establishment Panel.

Nowadays multiple imputation is also applied to impute missing values in hierarchical datasets, address confidentiality concerns, combine data from different sources, or correct measurement errors in surveys.

Using our results, multiply imputed datasets can be produced according to different admissible correlation structures between X and Y by using appropriate algorithms (e.g. NIBAS, see Rässler 2002; notice that since data fusion can be viewed as a problem of missing data, multiple imputation procedures are applicable in general).