Results 21 to 30 of about 1,808,699 (310)

On the Joys of Missing Data [PDF]

Journal of Pediatric Psychology, 2013
We provide conceptual introductions to missingness mechanisms--missing completely at random, missing at random, and missing not at random--and state-of-the-art methods of handling missing data--full-information maximum likelihood and multiple imputation--followed by a discussion of planned missing designs: Multiform questionnaire protocols, 2-method ...
Little, Todd D., Jorgensen, Terrence D., Lang, Kyle M., Moore, E. Whitney G.   +3 more
openaire   +3 more sources

INFERENCE AND MISSING DATA [PDF]

ETS Research Bulletin Series, 1975
ABSTRACTTwo results are presented concerning inference when data may be missing. First, ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, θ, is generally appropriate if and only if the missing data are “missing at random” and the observed data are “observed at random,” and then ...
openaire   +3 more sources

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study [PDF]

, 2010
Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the ...
Holder, Roger, Douglas G Altman, Holder, RL, Royston, Patrick, Royston, P, Marshall, A, Patrick Royston, Holder, Roger L., Altman, DG, Roger L Holder, Altman, Douglas G., Andrea Marshall, Marshall, A. (Andrea)   +12 more
core   +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

Missing data imputation using classification and regression trees [PDF]

PeerJ Computer Science
Background Missing data are common when analyzing real data. One popular solution is to impute missing data so that one complete dataset can be obtained for subsequent data analysis.
Cheng-Yang Chen, Yu-Wei Chang
doaj   +2 more sources

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

DEGAIN: Generative-Adversarial-Network-Based Missing Data Imputation

Information, 2022
Insights and analysis are only as good as the available data. Data cleaning is one of the most important steps to create quality data decision making. Machine learning (ML) helps deal with data quickly, and to create error-free or limited-error datasets.
Reza Shahbazian, Irina Trubitsyna
doaj   +1 more source

Multiple imputation with missing indicators as proxies for unmeasured variables: simulation study

BMC Medical Research Methodology, 2020
Background Within routinely collected health data, missing data for an individual might provide useful information in itself. This occurs, for example, in the case of electronic health records, where the presence or absence of data is informative.
Matthew Sperrin, Glen P. Martin
doaj   +1 more source

Missing Data Analysis in Regression

Applied Artificial Intelligence, 2022
Many of the datasets in real-world applications contain incompleteness. In this paper, we approach the effects and possible solutions to incomplete databases in regression, aiming to bridge a gap between theoretically effective algorithms.
C. G. Marcelino, G. M. C. Leite, P. Celes, C. E. Pedreira   +3 more
doaj   +1 more source

Imputation techniques for non-ordered categorical missing data [PDF]

, 2016
Philosophiae Doctor - PhDMissing data are common in survey data sets. Enrolled subjects do not often have data recorded for all variables of interest. The inappropriate handling of missing data may lead to bias in the estimates and incorrect inferences.
Karangwa, Innocent
core   +1 more source