Multiple Imputation of Multilevel Missing Data

Multiple Imputation for Missing Data

Multiple imputation provides a useful strategy for dealing with data sets with missing values. Instead of filling in a single value for each missing value, Rubin’s (1987) multiple imputation procedure replaces each missing value with a set of plausible values that represent the uncertainty about the right value to impute. These multiply imputed data sets are then analyzed by using standard proc...

Missing data imputation in multivariable time series data

Multivariate time series data are found in a variety of fields such as bioinformatics, biology, genetics, astronomy, geography and finance. Many time series datasets contain missing data. Multivariate time series missing data imputation is a challenging topic and needs to be carefully considered before learning or predicting time series. Frequent researches have been done on the use of diffe...

Multiple imputation: dealing with missing data.

In many fields, including the field of nephrology, missing data are unfortunately an unavoidable problem in clinical/epidemiological research. The most common methods for dealing with missing data are complete case analysis-excluding patients with missing data--mean substitution--replacing missing values of a variable with the average of known values for that variable-and last observation carri...

Multiple imputation of missing data using self organizing map ensembles

Multiple imputation of missing phenotype data for QTL mapping.

Missing phenotype data can be a major hurdle to mapping quantitative trait loci (QTL).Though in many cases experiments may be designed to minimize the occurrence of missing data,it is often unavoidable in practice; thus, statistical methods to account for missing data are needed.In this paper we describe an approach for conjoining multiple imputation and QTL mapping.Methods are applied to map g...