MOTIVATION Genotype imputation methods are used to enhance the resolution of genome-wide association studies, and thus increase the detection rate for genetic signals. Although most studies report all univariate summary statistics, many of them limit the access to subject-level genotypes. Because such an access is required by all genotype imputation methods, it is helpful to develop methods tha...

BACKGROUND Multiple imputation is a commonly used method for handling incomplete covariates as it can provide valid inference when data are missing at random. This depends on being able to correctly specify the parametric model used to impute missing values, which may be difficult in many realistic settings. Imputation by predictive mean matching (PMM) borrows an observed value from a donor wit...

In most situations, simple techniques for handling missing data (such as complete case analysis, overall mean imputation, and the missing-indicator method) produce biased results, whereas imputation techniques yield valid results without complicating the analysis once the imputations are carried out. Imputation techniques are based on the idea that any subject in a study sample can be replaced ...

Multiple imputation is a practically useful approach to handling incompletely observed data in statistical analysis. Parameter estimation and inference based on imputed full data have been made easy by Rubin's rule for result combination. However, creating proper imputation that accommodates flexible models for statistical analysis in practice can be very challenging. We propose an imputation f...

Missing data is an important issue in almost all fields of quantitative research. A nonparametric procedure that has been shown to be useful is the nearest neighbor imputation method. We suggest a weighted nearest neighbor imputation method based on Lq-distances. The weighted method is shown to have smaller imputation error than available NN estimates. In addition we consider weighted neighbor ...

To estimate the population mean with imputation, i.e. the technique of substituting missing data, there are a number of techniques available in literature like Ratio method of imputation, Compromised method of imputation, Mean method of imputation, Ahmed’s methods of imputation, F-T methods of imputation and so on. If population mean of auxiliary information is unknown then these methods are no...

Missing data are common in surveys regardless of research field, undermining statistical analyses and biasing results. One solution is to use an imputation method, which recovers missing data by estimating replacement values. Previously, we have evaluated the hot-deck k-Nearest Neighbour (kNN) method with Likert data in a software engineering context. In this paper, we extend the evaluation by ...

The aim of this paper is two-fold: to propose the imputation procedure named ABBN for replacing missing data in likert-type scales and to compare its performance with some well-known imputation methods. ABBN is a hot-deck imputation procedure which modifies the Approximate Bayesian Bootstrap method by sampling the donor in the neighbourhood of the nonrespondent. The comparison among the imputat...

Multiple imputation has entered mainstream practice for the analysis of incomplete data. We have used it extensively in a large Australian longitudinal cohort study, the Victorian Adolescent Health Cohort Study (1992–2008). Although we have endeavored to follow best practices, there is little published advice on this, and we have not previously examined the extent to which variations in our app...

Genotype-imputation methods provide an essential technique for high-resolution genome-wide association (GWA) studies with millions of single-nucleotide polymorphisms. For optimal design and interpretation of imputation-based GWA studies, it is important to understand the connection between imputation error and power to detect associations at imputed markers. Here, using a 2x3 chi-square test, w...