Results 91 to 100 of about 204,036 (213)
Discover DataProper handling of missing data is a necessity for all data driven research. Multiple imputation is considered as a superior approach to handle missing data.
Johané Nienkemper-Swanepoel
doaj +1 more source
MIDAS: A SAS Macro for Multiple Imputation Using Distance-Aided Selection of Donors
In this paper we describe MIDAS: a SAS macro for multiple imputation using distance aided selection of donors which implements an iterative predictive mean matching hot-deck for imputing missing data.
Juned Siddique, Ofer Harel
core
Estimation of the Distribution of Hourly Pay from Household Survey Data: The Use of Missing Data Methods to Handle Measurement Error [PDF]
Measurement errors in survey data on hourly pay may lead to serious upward bias in low pay estimates. We consider how to correct for this bias when auxiliary accurately measured data are available for a subsample.
Chris Skinner, Gabriele Beissel-Durrant
core
Multiple imputation of right-censored wages in the German IAB Employment Sample considering heteroscedasticity [PDF]
"In many large data sets of economic interest, some variables, as wages, are top-coded or right-censored. In order to analyze wages with the German IAB employment sample we first have to solve the problem of censored wages at the upper limit of the ...
Büttner, Thomas, Rässler, Susanne
core
Assessing the reasonableness of an imputation model [PDF]
Multiple imputation is a popular way of dealing with missing values under the missing at random (MAR) assumption. Imputation models can become quite complicated, for instance, when the model of substantive interest contains many interactions or when the ...
Maarten L. Buis
core
Imputation of continuous variables missing at random using the method of simulated scores
For multivariate datasets with missing values, we present a procedure of statistical inference and state its "optimal" properties. Two main assumptions are needed: (1) data are missing at random (MAR); (2) the data generating process is a multivariate ...
Neri, Laura, Calzolari, Giorgio
core
The Data Quality Concept of Accuracy in the Context of Public Use Data Sets [PDF]
Like other data quality dimensions, the concept of accuracy is often adopted to characterise a particular data set. However, its common specification basically refers to statistical properties of estimators, which can hardly be proved by means of a ...
Martin Spieß, Carsten Kuchler
core
PLoS Computational Biology, 2020 Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of haplotypes for thousands of individuals, which is known as a haplotype reference panel.
Kaname Kojima +6 more
doaj
yaImpute: An R Package for kNN Imputation
This article introduces yaImpute, an R package for nearest neighbor search and imputation. Although nearest neighbor imputation is used in a host of disciplines, the methods implemented in the yaImpute package are tailored to imputation-based forest ...
Andrew O. Finley, Nicholas L. Crookston
core
A new approach for disclosure control in the IAB Establishment Panel : multiple imputation for a better data access [PDF]
"For micro-datasets considered for release as scientific or public use files, statistical agencies have to face the dilemma of guaranteeing the confidentiality of survey respondents on the one hand and offering sufficiently detailed data on the other ...
Rässler, Susanne +4 more
core