Estimating the parameters of a regression model of interest is complicated by missing data on the variables in that model. Multiple imputation is commonly used to handle these missing data. Joint model multiple imputation and full-conditional specification multiple imputation are known to yield imputed data with the same asymptotic distribution when the conditional models of full-conditional sp...

In order to study the placement of an imputation step, we create a logical imputation operator (along with respective physical instances) and incorporate its placement as part of the query plan optimization process in SimpleDB. We introduce measures for information loss and runtime for imputation operations, which outline the main trade-offs in the imputation placement. We add these measures in...

Multiple imputation, as described by Rubin, has seen a wide variety of applications. Counterexamples, presented by Fay (1991), and new methods, such as those of J.N.K. Rao and J. Shao, that can asymptotically disagree with the multiple imputation approach, have raised questions about the validity of multiple imputation. This paper identifies critical restrictions on the practical application of...

In this study, we consider the nonparametric quantile regression model with the covariates Missing at Random (MAR). Multiple imputation is becoming an increasingly popular approach for analyzing missing data, which combined with quantile regression is not well-developed. We propose an effective and accurate two-stage multiple imputation method for the model based on the quantile regression, whi...

The potential for imputed genotypes to enhance an analysis of genetic data depends largely on the accuracy of imputation, which in turn depends on properties of the reference panel of template haplotypes used to perform the imputation. To provide a basis for exploring how properties of the reference panel affect imputation accuracy theoretically rather than with computationally intensive imputa...

Imputation of missing data is important in many areas, such as reducing non-response bias in surveys and maintaining medical documentation. Estimating the uncertainty inherent in the imputed values is one way of evaluating the results of the imputation process. This paper presents a new method for the estimation of imputation uncertainty, which can be implemented as part of any imputation metho...

A common problem encountered by many data mining techniques is the missing data. A missing data is defined as an attribute or feature in a dataset which has no associated data value. Correct treatment of these data is crucial, as they have a negative impact on the interpretation and result of data mining processes. Missing value handling techniques can be grouped into four categories, namely, c...

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...

BACKGROUND Imputation techniques used to handle missing data are based on the principle of replacement. It is widely advocated that multiple imputation is superior to other imputation methods, however studies have suggested that simple methods for filling missing data can be just as accurate as complex methods. The objective of this study was to implement a number of simple and more complex imp...

Multiple imputation is used to create values for missing family income data in the National Survey on Recreation and the Environment. We present an overview of the survey and a description of the missingness pattern for family income and other key variables. We create a logistic model for the multiple imputation process and to impute data sets for family income. We compare results between estim...