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Research Article | 03-March-2021

An application of a complex measure to model–based imputation in business statistics

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

Andrzej Młodak

Statistics in Transition New Series, Volume 22 , ISSUE 1, 1–28

Article | 28-May-2019


The estimation of population parameters might be quite laborious and inefficient, when the sample data have missing values. In comparison follow-up visits, the method of imputation has been found to be a cheaper procedure from a cost point of view. In the present study, we can enhance the performance of imputation procedures by utilizing the raw moments of the auxiliary information rather than their ranks, especially, when the ranking of the auxiliary variable is expensive or difficult to do so

Muhammed Umair Sohail, Javid Shabbir, Farinha Sohil

Statistics in Transition New Series, Volume 20 , ISSUE 1, 21–40

Research Communicate | 27-May-2018


In this paper, a product exponential method of imputation has been suggested and their corresponding resultant point estimator has been proposed for estimating the population mean in sample surveys. The expression of bias and the mean square error of the suggested estimator has also been derived, up to the first order of large sample approximations. Compared with the mean imputation method, Singh and Deo (Statistical Papers (2003)) and Adapted estimator (Bahl and Tuteja (1991)), the simulation

Shakti Prasad

Statistics in Transition New Series, Volume 19 , ISSUE 1, 159–166

Research Article | 13-December-2019


Large-scale complex surveys typically contain a large number of variables measured on an even larger number of respondents. Missing data is a common problem in such surveys. Since usually most of the variables in a survey are categorical, multiple imputation requires robust methods for modelling high-dimensional categorical data distributions. This paper introduces the 3-stage Hybrid Multiple Imputation (HMI) approach, computationally efficient and easy to implement, to impute complex survey

Humera Razzak, Christian Heumann

Statistics in Transition New Series, Volume 20 , ISSUE 4, 33–58

Research Article | 10-January-2020

Imputation of missing network data: Some simple procedures

exponential random graph models, and imputation methods. In this paper we focus on the latter group of methods, and investigate the use of some simple imputation procedures to handle missing network data. The results of a simulation study show that ignoring the missing data can have large negative effects on structural properties of the network. Missing data treatment based on simple imputation procedures, however, does also have large negative effects and simple imputations can only successfully correct

Mark Huisman

Journal of Social Structure, Volume 10 , ISSUE 1, 1–29

Sampling Methods | 20-November-2017


We propose a variance estimator based on factor type imputation in the presence of non-response. Properties of the proposed classes of estimators are studied and their optimality conditions are derived. The proposed classes of factor type ratio estimators are shown to be more efficient than some of the existing estimators, namely, the usual unbiased estimator of variance, ratio-type, dual to ratio type and ratio cum dual to ratio estimators. Their performances are assessed on the basis of


Statistics in Transition New Series, Volume 18 , ISSUE 3, 375–392

Article | 08-December-2021

Unreported standard errors in meta-analysis

A study that would otherwise be eligible is commonly excluded from a meta-analysis when the standard error of its treatment-effect estimator, or the estimate of the variance of the outcomes, is not reported and cannot be recovered from the available information. This is wasteful when the estimate of the treatment effect is reported. We assess the loss of informa tion caused by this practice and explore methods of imputation for the missing variance. The methods are illustrated on two sets of

Nicholas T. Longford

Statistics in Transition New Series, Volume 22 , ISSUE 4, 1–17

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