Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/236813 
Year of Publication: 
2021
Citation: 
[Journal:] Statistics in Transition New Series [ISSN:] 2450-0291 [Volume:] 22 [Issue:] 1 [Publisher:] Exeley [Place:] New York [Year:] 2021 [Pages:] 1-28
Publisher: 
Exeley, New York
Abstract: 
When faced with missing data in a statistical survey or administrative sources, imputation is frequently used in order to fill the gaps and reduce the major part of bias that can affect aggregated estimates as a consequence of these gaps. This paper presents research on the efficiency of model-based imputation in business statistics, where the explanatory variable is a complex measure constructed by taxonomic methods. The proposed approach involves selecting explanatory variables that fit best in terms of variation and correlation from a set of possible explanatory variables for imputed information, and then replacing them with a single complex measure (meta-feature) exploiting their whole informational potential. This meta-feature is constructed as a function of a median distance of given objects from the benchmark of development. A simulation study and empirical study were used to verify the efficiency of the proposed approach. The paper also presents five types of similar techniques: ratio imputation, regression imputation, regression imputation with iteration, predictive mean matching and the propensity score method. The second study presented in the paper involved a simulation of missing data using IT business data from the California State University in Los Angeles, USA. The results show that models with a strong dependence on functional form assumptions can be improved by using a complex measure to summarize the predictor variables rather than the variables themselves (raw or normalized).
Subjects: 
complex measure
ratio imputation
regression imputation
predictive mean matching
propensity score method
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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