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...

We consider an empirical likelihood inference for parameters defined by general estimating equations when some components of the random observations are subject to missingness. As the nature of the estimating equations is wide ranging, we propose a nonparametric imputation of the missing values from a kernel estimator of the conditional distribution of the missing variable given the always obse...

MOTIVATION Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level. RESULTS We present a statistical model that carefully accoun...

Bayesian multiple imputation and maximum likelihood provide useful strategy for dealing with dataset including missing values. Imputation methods affect the significance of test results and the quality of estimates. In this paper, the general procedures of multiple imputation and maximum likelihood described which include the normal-based analysis of a multiple imputed dataset. A Monte Carlo si...

Traffic engineering studies such as validating Highway Capacity Manual (HCM) models require complete and reliable field data. However, the wealth of Intelligent Transportation Systems (ITS) data is sometimes rendered useless for these purposes because of missing values in the data. Many imputation techniques have been developed in the past with virtually all of them imputing a single value for ...

We consider the application of multiple imputation to data containing not only partially missing categorical and continuous variables, but also partially missing ‘semicontinuous’ variables (variables that take on a single discrete value with positive probability but are otherwise continuously distributed). As an imputation model for data sets of this type, we introduce an extension of the stand...

Title of Document: THE MISSING VALUE PROBLEM: A REVIEW AND CASE STUDY Jing Zhou, M. A., 2006 Directed By: Professor Paul J. Smith, Statistics Program, Department of Mathematics. The purpose of this thesis is to review methods of imputation and apply them to data collected by Equal Employment Opportunity Commission (EEOC). First, I discuss several imputation methods and review theory of multiple...

We consider a data set with missing observations but known auxiliaries for the sample and develop a real donor imputation. For each unit with missing observations we construct a distribution over a set of possible donors. We want the expectation (or distribution) to be chosen so that the expectation (or distribution) of the imputed values should equal the distribution of the units’ true values....

The problem of estimating the population mean using calibration estimators when some observations on the study and auxiliary characteristics are missing from the sample, is considered. Some new classes of estimators are proposed for any sampling design. These new classes employ to all observation (incomplete cases too) in the estimation without using any imputation techniques. On the basis of p...