BACKGROUND In the past few years, imputation approaches have been mainly used in population-based designs of genome-wide association studies, although both family- and population-based imputation methods have been proposed. With the recent surge of family-based designs, family-based imputation has become more important. Imputation methods for both designs are based on identity-by-descent (IBD) ...

In the Pre-Elementary Education Longitudinal Study (PEELS), imputation of item missing data was done using AutoImpute (AI) software, which uses semi-parametric modeling to form imputation classes. In this paper, we summarize PEELS experience with AI, investigate the bias aspect of the imputed data for the PEELS teacher questionnaire data, and study the variance estimation of imputed data using ...

Commonly in survey research, multiple, different analyses are conducted by one or more than one researcher on the same data set. The conclusions from these analyses should be consistent despite the presence of missing data. Multiple imputation is frequently used to ensure consistency of analyses. Two methods for multiple imputation of missing data are a combination of hot deck and regression im...

Missing values are common in data analysis and present a usability challenge. Users are forced to pick between removing tuples withmissing values or creating a cleaned version of their data by applying a relatively expensive imputation strategy. Our system, ImputeDB, incorporates imputation into a costbased query optimizer, performing necessary imputations onthe-fly for eachquery. This allows u...

BACKGROUND Multiple imputation has become very popular as a general-purpose method for handling missing data. The validity of multiple-imputation-based analyses relies on the use of an appropriate model to impute the missing values. Despite the widespread use of multiple imputation, there are few guidelines available for checking imputation models. ANALYSIS In this paper, we provide an overvi...

In this paper, the author designs an efficient method for imputing iteratively missing target values with semi-parametric kernel regression imputation, known as the semi-parametric iterative imputation algorithm (SIIA). While there is little prior knowledge on the datasets, the proposed iterative imputation method, which impute each missing value several times until the algorithms converges in ...

THE ANALYSIS OF MISSING DATA IN PUBLIC USE SURVEY DATABASES: A SURVEY OF STATISTICAL METHODS Ping Xu November 20, 2004 Missing data is very common in survey research. However, currently few guidelines exist with regard to the diagnosis and remedy to missing data in survey research. The goal of the thesis was to investigate properties and effects of three selected missing data handling technique...

Multiple imputation is increasingly recommended in epidemiology to adjust for the bias and loss of information that may occur in analyses restricted to study participants with complete data (‘‘complete-case analyses’’). However, little guidance is available on applying the method, including which variables to include in the imputation model and the number of imputations needed. Here, the author...

Missing values are very common in real-world datasets for a variety of reasons. Deleting data points with missing values can negatively impact the performance of data analysis methods (e.g., machine learning, data mining). Using a human expert to restore the missing values is expensive and time consuming. The alternative is to impute the missing values during data preprocessing using the known ...