Results 81 to 90 of about 204,036 (213)

Improving the performance of Bayesian networks in non-ignorable missing data imputation

Kuwait Journal of Science, 2013
The issue of missing data may arise for researchers who deal with data gathering problems. Bayesian networks are one of the proposed methods that have been recently used in missing data imputation.
P. NILOOFAR, M. GANJALI, AND M.R. FARID ROHANI   +2 more
doaj  

Improving genomic prediction accuracy of pig reproductive traits based on genotype imputation using preselected markers with different imputation platforms

Animal
Genomic prediction has been widely applied to the pig industry and has greatly accelerated the progress of genetic improvement in pigs. With the development of sequencing technology and price reduction, more and more genotype imputation panels of pig ...
J. Sun, J. Wei, Y. Pan, M. Cao, X. Li, J. Xiao, G. Yang, T. Yu   +7 more
doaj   +1 more source

Multiple Imputation for Longitudinal Data: A Tutorial

Statistics in Medicine
ABSTRACTLongitudinal studies are frequently used in medical research and involve collecting repeated measures on individuals over time. Observations from the same individual are invariably correlated and thus an analytic approach that accounts for this clustering by individual is required.
Rushani Wijesuriya, Margarita Moreno‐Betancur, John B. Carlin, Ian R. White, Matteo Quartagno, Katherine J. Lee   +5 more
openaire   +3 more sources

Measuring Inequality Using Censored Data: A Multiple Imputation Approach [PDF]

To measure income inequality with right censored (topcoded) data, we propose multiple imputation for censored observations using draws from Generalized Beta of the Second Kind distributions to provide partially synthetic datasets analyzed using complete ...
Shuaizhang Feng, Richard V. Burkhauser, Jeff Larrimore, Stephen P. Jenkins   +3 more
core   +4 more sources

Flexible imputation toolkit for electronic health records

Scientific Reports
Missing data in electronic health records (EHRs) poses a significant challenge for analysis. This study introduces Pympute, a comprehensive Python package designed for efficient and robust missing value imputation for EHRs.
Alireza Vafaei Sadr, Jiang Li, Wenke Hwang, Mohammed Yeasin, Ming Wang, Harold Lehmann, Ramin Zand, Vida Abedi   +7 more
doaj   +1 more source

Missing.... presumed at random: cost-analysis of incomplete data [PDF]

, 2002
When collecting patient-level resource use data for statistical analysis, for some patients and in some categories of resource use, the required count will not be observed. Although this problem must arise in most reported economic evaluations containing
Clark, T, Andrew Briggs, Philip Clarke, Briggs, AH, Clark, PM, Wolstenholme, J., Briggs, A., Wolstenholme, Jane, Clarke, P., Wolstenholme, J, Briggs, Andrew, Clark, Taane, Clark, T., Jane Wolstenholme, Taane Clark, Clarke, Philip   +15 more
core   +1 more source

Representative Wealth Data for Germany from the German SOEP: The Impact of Methodological Decisions around Imputation and the Choice of the Aggregation Unit [PDF]

The definition and operationalization of wealth information in population surveys and the corresponding microdata requires a wide range of more or less normative assumptions. However, the decisions made in both the pre- and post-data-collection stage may
Markus M. Grabka, Joachim R. Frick, Eva M. Sierminska   +2 more
core   +2 more sources

Semiparametric Regression Analysis under Imputation for Missing Response Data [PDF]

We develop inference tools in a semiparametric regression model with missing response data. A semiparametric regression imputation estimator, a marginal average estimator and a (marginal) propensity score weighted estimator are defined.
Qihua Wang, Wolfgang Haerdle, Oliver Linton   +2 more
core  

A Stochastic Method for Estimating Imputation Accuracy

This thesis describes a novel imputation evaluation method and shows how this method can be used to estimate the accuracy of the imputed values generated by any imputation technique.
Solomon, Norman
core  

The Impact of Multiple Imputation of Coarsened Data on Estimates on the Working Poor in South Africa [PDF]

South African household surveys typically contain coarsened earnings data, which consist of a mixture of missing earnings values, point responses and interval-censored responses.
Vermaark, Claire
core