Imputation using a regression model is a method to preserve the correlation among variables and to provide imputed point estimators. We discuss the implementation of regression imputation using fractional imputation. By a suitable choice of fractional weights, the fractional regression imputation can take the form of hot deck fractional imputation, thus no artificial values are constructed afte...

process of machine learning and data mining when certain values are missed. Among extant imputation techniques, kNN imputation algorithm is the best one as it is a model free and efficient compared with other methods. However, the value of k must be chosen properly in using kNN imputation. In particular, when some nearest neighbors are far from a missing data, the kNN imputation algorithms are ...

background: diagnostic models are frequently used to assess the role of risk factors on disease complications, and therefore to avoid them. missing data is an issue that challenges the model making. the aim of this study was to develop a diagnostic model to predict death in hiv/ aids patients when missing data exist. methods: hiv patients (n=1460) referred to voluntary consoling and testing cen...

This study compares imputation methods (single and multiple) to examine the role of perceived stress in the relationship between social support and mood, and tested whether mediator effects influenced the relationship. The cross-sectional data reported here was collected in an experimental design with repeated measures with mothers of children who had been hospitalized in a child psychiatric un...

Missing value imputation is one of the biggest tasks of data pre-processing when performing data mining. Most medical datasets are usually incomplete. Simply removing the cases from the original datasets can bring more problems than solutions. A suitable method for missing value imputation can help to produce good quality datasets for better analysing clinical trials. In this paper we explore t...

This article introduces yaImpute, an R package for nearest neighbor search and imputation. Although nearest neighbor imputation is used in a host of disciplines, the methods implemented in the yaImpute package are tailored to imputation-based forest attribute estimation and mapping. The impetus to writing the yaImpute is a growing interest in nearest neighbor imputation methods for spatially ex...

Appropriate imputation inference requires both an unbiased imputation estimator and an unbiased variance estimator. The commonly used variance estimator, proposed by Rubin, can be biased when the imputation and analysis models are misspecified and/or incompatible. Robins and Wang proposed an alternative approach, which allows for such misspecification and incompatibility, but it is considerably...

BACKGROUND Multiple imputation is frequently used to deal with missing data in healthcare research. Although it is known that the outcome should be included in the imputation model when imputing missing covariate values, it is not known whether it should be imputed. Similarly no clear recommendations exist on: the utility of incorporating a secondary outcome, if available, in the imputation mod...

The usual methods for analyzing case-cohort studies rely on sometimes not fully efficient weighted estimators. Multiple imputation might be a good alternative because it uses all the data available and approximates the maximum partial likelihood estimator. This method is based on the generation of several plausible complete data sets, taking into account uncertainty about missing values. When t...

1. Missing data problems are ubiquitous in many fields, including official statistics, where one of the common treatments of missing data is ratio imputation (de Waal et al., 2011; Thompson & Washington, 2012; Office for National Statistics, 2014). On the other hand, multiple imputation has been the recommended practice from statisticians (Rubin, 1987; Little & Rubin, 2002). Among statisticians...