Results 61 to 70 of about 204,036 (213)

Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines [PDF]

, 2009
Background: Multiple imputation (MI) provides an effective approach to handle missing covariate data within prognostic modelling studies, as it can properly account for the missing data uncertainty. The multiply imputed datasets are each analysed using
Holder, Roger, Douglas G Altman, Altman Douglas G, Holder, RL, Royston, Patrick, Royston, P, Marshall, A, Patrick Royston, Marshall Andrea, Holder, Roger L., Roger L Holder, Altman, DG, Altman, Douglas G., Royston Patrick, Andrea Marshall, Marshall, A. (Andrea), Holder Roger L, Altman, Doug   +17 more
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Optimizing genotype imputation pipeline for low-coverage whole genome sequencing data in spotted sea bass and its application in genomic prediction

Aquaculture Reports
Genotype imputation following low-coverage whole genome sequencing (lcWGS) data offers a cost-effective approach for genotyping large populations, with significant potential to accelerate genomic selection in breeding programs.
Chong Zhang, Yonghang Zhang, Pengyu Li, Cong Liu, Lingyu Wang, Yani Dong, Donglei Sun, Xin Qi, Haishen Wen, Kaiqiang Zhang, Shaosen Yang, Yun Li   +11 more
doaj   +1 more source

Variable selection with Random Forests for missing data [PDF]

, 2013
Variable selection has been suggested for Random Forests to improve their efficiency of data prediction and interpretation. However, its basic element, i.e.
Hapfelmeier, Alexander, Ulm, Kurt
core   +1 more source

Outcome-sensitive multiple imputation: a simulation study

BMC Medical Research Methodology, 2017
Background Multiple imputation is frequently used to deal with missing data in healthcare research. Although it is known that the outcome should be included in the imputation model when imputing missing covariate values, it is not known whether it should
Evangelos Kontopantelis, Ian R. White, Matthew Sperrin, Iain Buchan   +3 more
doaj   +1 more source

Calibrated imputation of numerical data under linear edit restrictions

, 2008
A common problem faced by statistical offices is that data may be missing from collected data sets. The typical way to overcome this problem is to impute the missing data.
Shlomo, Natalie, Pannekoek, Jeroen, Ton De Waal, De Waal, Ton, Natalie Shlomo, Jeroen Pannekoek   +5 more
core  

Taking "Don't Knows" as Valid Responses: A Complete Random Imputation of Missing Data [PDF]

Incomplete data is a common problem of survey research. Recent work on multiple imputation techniques has increased analysts' awareness of the biasing effects of missing data and has also provided a convenient solution.
Martin Kroh
core  

Evaluating Performance of Missing Data Imputation Methods in IRT Analyses

International Journal of Assessment Tools in Education, 2018
Missing data is a common problem in datasets thatare obtained by administration of educational and psychological tests. It is widelyknown that existence of missing observations in data can lead to serious problemssuch as biased parameter estimates and ...
Ömür Kaya Kalkan, Yusuf Kara, Hülya Kelecioğlu   +2 more
doaj   +1 more source

A Markov Chain Monte Carlo Multiple Imputation Procedure for Dealing with Item Nonresponse in the German SAVE Survey [PDF]

Important empirical information on household behavior is obtained from surveys. However, various interdependent factors that can only be controlled to a limited extent lead to unit and item nonresponse, and missing data on certain items is a frequent ...
Daniel Schunk
core   +2 more sources

Chained equations and more in multiple imputation in Stata 12 [PDF]

I present the new Stata 12 command, mi impute chained, to perform multivariate imputation using chained equations (ICE), also known as sequential regression imputation. ICE is a flexible imputation technique for imputing various types of data.
Yulia Marchenko
core  

Handling missing data in research

Perspectives in Clinical Research
Missing data are an inevitable part of research and lead to a decrease in the size of the analyzable population, and biased and imprecise estimates. In this article, we discuss the types of missing data, methods to handle missing data and suggest ways in
Priya Ranganathan, Sally Hunsberger
doaj   +1 more source