Sample surveys typically gather information on a sample of units from a finite population and assign survey weights to the sampled units. Survey frequently have missing values for some variables for some units. Fractional regression imputation creates multiple values for each missing value by adding randomly selected empirical residuals to predicted values. Fractional imputation methods assign ...

Outcome misclassification is widespread in epidemiology, but methods to account for it are rarely used. We describe the use of multiple imputation to reduce bias when validation data are available for a subgroup of study participants. This approach is illustrated using data from 308 participants in the multicenter Herpetic Eye Disease Study between 1992 and 1998 (48% female; 85% white; median a...

Missing data is universal and methods to deal with it far ranging from simply ignoring it to using complex modelling strategies such as multiple imputation and maximum likelihood estimation.Missing data has only been effectively imputed by machines via statistical/machine learning models. In this paper we set to answer an important question ”Can humans perform reasonably well to fill in missing...

Multiple imputation is a strategy for the analysis of incomplete data such that the impact of the missingness on the power and bias of estimates is mitigated. When data from multiple studies are collated, we can propose both within-study and multilevel imputation models to impute missing data on covariates. It is not clear how to choose between imputation models or how to combine imputation and...

BACKGROUND The appropriate handling of missing covariate data in prognostic modelling studies is yet to be conclusively determined. A resampling study was performed to investigate the effects of different missing data methods on the performance of a prognostic model. METHODS Observed data for 1000 cases were sampled with replacement from a large complete dataset of 7507 patients to obtain 500...

Missing data is a significant problem impacting all domains. State-of-the-art framework for minimizing missing data bias is multiple imputation, for which the choice of an imputation model remains nontrivial. We propose a multiple imputation model based on overcomplete deep denoising autoencoders. Our proposed model is capable of handling different data types, missingness patterns, missingness ...

1 This work is primarily focused on missing traffic sensor data imputation for the purpose of improving the 2 coverage and accuracy of traffic analysis and performance estimation. Missing data, whether attributable 3 to hardware failure or error detection and removal, is a constant problem in loop and other traffic detector 4 datasets. As the rate of missingness increases, the treatment of miss...

Multiple imputation is rapidly becoming a popular method for handling missing data, especially with easy-to-use software like PROC MI. In this paper, however, I argue that maximum likelihood is usually better than multiple imputation for several important reasons. I then demonstrate how maximum likelihood for missing data can readily be implemented with the following SAS procedures: MI, MIXED, ...

The value of knowledge inferred from information databases is critically dependent on the quality of data. We present multiple imputation as a reliable and consistent imputation technique for handling missing data in a numeric dependent variable in software metrics data sets. Experiments were conducted using multiple, mean, k-Nearest Neighbors, regression, and REPTree to impute missing values i...

Non-response in establishment surveys is a very important problem that can bias results of statistical analysis. The bias can be considerable when the survey data is used to do multivariate analysis that involve several variables with different response rates, which can reduce the effective sample size considerably. Fixing the non-response, however, could potentially cause other econometric pro...