Multiple imputation
Multiple imputation for estimating hazard ratios and predictive abilities in case-cohort surveys.
... The imputation model must reflect the association between the incomplete variable, the outcome and the other explanatory ...the imputation model. On the other hand, multiple imputation ...
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Estimating the success of an in vitro fertilization programme using multiple imputation.
... Olivius, multiple imputation has the advantage of providing completed data sets, which include both observed and imputed ...First, multiple imputation worked well with two IVF ...the ...
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Model selection to improve multiple imputation for handling high rate missingness in a water quality dataset
... of Multiple Imputation (MI), defined a conceptual framework for characterizing missing data that allows to distin- guish various types and to determine when missing data can be ignored ( Little & Rubin, ...
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A peptide-level multiple imputation strategy accounting for the different natures of missing values in proteomics data
... This feature is more clearly observed when comparing either SLSA to M I(SLSA, IGCDA), or M LE to M I(M LE, IGCDA) when b = 3. This makes sense for the follow- ing reason: When b = 1.5, this feature is not observed since ...
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Multiple imputation for demographic hazard models with left-censored predictor variables: Application to employment duration and fertility in the EU-SILC.
... that multiple imputation (MI), a method typically used to handle non-response (Johnson and Young 2011), can be a general solution to the problem of left censoring in demographic hazard ...for ...
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Dealing with missing data in family-based association studies: a multiple imputation approach
... In this context, it is of interest to develop methods to test for association with genetic risk factors in the framework of traditional statistical packages such as Stata, S-Plus/R or SAS, which allow the inclusion of ...
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Simulation-based comparative performance of multiple imputation methods for incomplete longitudinal ordinal datasets
... Introduction Multiple imputation (MI) is now a reference solution for handling missing data [1]. The idea is to replace each missing value not only once but by a set of M (M > 1) plausible values, thus ...
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Simulation-based comparative performance of multiple imputation methods for incomplete longitudinal ordinal datasets
... 2. For each missing value, 𝑌 𝑖𝑗 𝑚𝑖𝑠 , compute Pr 𝑌 𝑖𝑗 𝑚𝑖𝑠 = 𝑘 𝑥 𝑖𝑗 ∗ , 𝑘 = 1, … , 𝐾. 3. Impute each missing value, 𝑌 𝑖𝑗 𝑚𝑖𝑠 , by randomly drawing from a multinomial distribution with probabilities derived in step 2. ...
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A three-source capture-recapture estimate of the number of new HIV diagnoses in children in France from 2003--2006 with multiple imputation of a variable of heterogeneous catchability.
... a multiple imputation (MI) method, in which the distribution of the observed data is used to estimate a set of plausible values for the missing observations ...[11]. Multiple data sets were created, ...
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Multiple imputation for demographic hazard models with left-censored predictor variables
... Using multiple imputation, however, we are additionally able to include birth-exposure intervals between the second and third wave in a model that uses employment status from two previous ...
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Implementing Rubin's Alternative Multiple Imputation Method for Statistical Matching in Stata
... the multiple imputation methods proposed in Rubin ...continuous imputation variables” while the chained command ...multivariate imputation using chained equations) is “another multivariate ...
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Multiple imputation for estimation of an occurrence rate in cohorts with attrition and discrete follow-up time points: a simulation study.
... of multiple imputation in this simulation study are very satisfactory and ...of multiple imputation could provide a measure of the impact of attrition on the ...
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2013 — Impact analysis of a multiple imputation technique for handling missing value in the ISBSG repository of software projects
... Hot-Deck Imputation: the technique of hot deck imputation (Little et Rubin, 2002), (Kim et Wayne, 2004), (Fuller et Kim, 2005) and (Ford, 1983) is called fractional hot deck ...Hot-deck imputation ...
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Does pattern mixture modelling reduce bias due to informative attrition compared to fitting a mixed effects model to the available cases or data imputed using multiple imputation?: a simulation study
... restricting imputation to a short time window. Also, the results from a simulation study found increased precision when analysing time-dependent explanatory variables im- puted using the two-fold FCS algorithm ...
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Improving Binary Classifier Performance Through an Informed Sampling Approach and Imputation
... Can imputation techniques improve the prediction accuracy of classification tasks with a fixed rate of observations per record? Previous research has solely relied on non-fixed (variable) rate of observations per ...
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A Comparison of Imputation Strategies in Cluster Randomized Trials with Missing Binary Outcomes
... and multiple imputation ...Both multiple imputation with a random-effects logistic regression model or classical logistic regression provided unbiased estimates of the intervention ...for ...
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Heckman imputation models for binary or continuous MNAR outcomes and MAR predictors
... Background: Multiple imputation by chained equations (MICE) requires specifying a suitable conditional imputation model for each incomplete variable and then iteratively imputes the missing ...an ...
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Imputation multiple par analyse factorielle : Une nouvelle méthodologie pour traiter les données manquantes
... a multiple imputation method is based on a single imputation ...the imputation model, a multiple impu- tation method requires generating a set of M parameters (b θ1, ...parameters. ...
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Multiple hot-deck imputation for network inference from RNA sequencing data
... novel imputation method, hot-deck multiple imputation (hd-MI), that artificially increases the sample size and thus improves the reliability of network ...
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PRIMITIVE MULTIPLE SCHEMES
... primitive multiple schemes are very ...primitive multiple schemes (for m = 2, in multiplicities 2 and 4), and these two schemes are not quasi-projective (the scheme of multiplicity 2 appears already in [2], ...
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